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Global climate changes have led to frequent and recurrent heat stress, which has seriously affected the world maize production. The experiment presented in this paper was carried out during the maize‐growing season in 2021 and 2022 at the Huang–Huai–Hai Region Maize Science and Technology Innovation Center (36°09′N, 117°09′E) in Tai'an, Shandong province, China. The test site is located in the semi‐humid warm temperate continental monsoon climate zone, soil is brown loam. We selected different heat‐sensitive maize varieties: heat‐tolerant variety Zhengdan 958 (ZD958) and heat‐sensitive variety Xianyu335 (XY335) to study the stress effects of high temperature on the development of tassel in the key stage of its differentiation (the 12‐leaf stage, V12). We found that exposure to heat stress during the V12 stage significantly reduced the tassel size and the anther dehiscence area, making it difficult to disperse pollen, thus effectively reducing pollen production. In addition, heat stress had significant negative effects on pollen development, resulting in deformed pollen and lower pollen viability and germination rate. Our results also showed that heat stress significantly increased the activities of key reactive oxygen species‐scavenging enzymes including superoxide dismutase, peroxidase and catalase, and that the malondialdehyde and H2O2 content in tassels exposed to heat stress were significantly higher than those control group, where it should be noted that for the XY335, these effects were more pronounced than for the ZD958. The endogenous hormone content of tassels exhibited a variety of responses. After heat stress, the zeatin and salicylic acid content in tassels of both maize varieties were significantly lower than those in the respective control groups, while the abscisic acid and gibberellin acid content were significantly higher. With respect to the jasmonic acid and 3‐indoleacetic acid content, the two maize varieties exhibited opposite responses: in tassels from ZD958 plants subjected to heat stress were higher than those in the respective control group, while in tassels from XY335 plants, they were lower. The observed changes in protective enzyme activities and endogenous hormone contents induced by heat stress significantly affected the development of maize tassels and significantly reduced the amount, activity and germination rate of pollen. [ABSTRACT FROM AUTHOR]

• This study analyzed the spatiotemporal influence of drivers to vegetation variation. • Identify the effects of vegetation variation on different soil erosion types. • We provide a driver-pattern-effect connection of vegetation variation. Vegetation is at the nexus of water, soil, and the atmosphere, making it a significant indicator of climatic variability and shifts in human activity. The spatial attribution analysis of vegetation changes may help us to identify and estimate the relationship between variation in vegetation and different erosion types, and their correspondence may be in the antecedents and consequences of vegetation dynamics. Here, based on time series datasets for the Bashang Plateau and northern North China's mountainous region from 1998 to 2015, we used trend analysis, partial correlation analysis, and univariate linear regression to investigate the sustainability of vegetation variations and to determine the driving mechanisms of climatic and anthropogenic factors in vegetation dynamics at the regional scale. Further, we applied correlation analysis and models (i.e., USLE and RWEQ) to detect the spatial heterogeneity of soil erosion dynamics and the effects of vegetation coverage variation on the dynamics of water erosion, wind erosion, and wind-water erosion. Our results revealed a significant increasing trend in Normalized Difference Vegetation Index (NDVI) and the improved area accounted for more than 80% of the study area. The response of vegetation variation to climatic factors displayed a high correlation with precipitation for a 1-month lag in the semi-humid area, especially in forestland, and with temperature for a 0-month lag in the semi-arid area, particularly in farmland. These results suggest human activity has had a greater effect on vegetation variations than climatic factors during 2011–2015, and that human activity was the dominant factor controlling vegetation changes in terms of the spatial distribution of major drivers; notably, the implementation of the vegetation restoration projects affected the spatiotemporal patterns of vegetation cover. During 1998–2015, the increased vegetation coverage played a dominant role in reducing water erosion of the forestland, and it had a vital impact on lessening wind erosion in open land and sparsely vegetated areas, while the increased fractional vegetation coverage (FVC) may have been a contributing factor to aggravated soil erosion in the wind-water erosion zone of grassland and forestland. Hence, the importance of the implementation of revegetation projects on soil erosion reduction should not be underestimated. [ABSTRACT FROM AUTHOR]

Thermal comfort evaluations in the built environment are essential to occupant's satisfaction and also building energy consumption. Traditionally, thermal comfort has been assessed by Fanger's PMV model that was developed based on extensive laboratory tests. However, it's been found that PMV predictions are not good in field studies, especially in buildings with natural ventilation. In this paper, machine learning (ML) algorithms were used to predicted occupants' thermal comfort votes (TCV) and thermal sensation votes (TSV), using 5512 sets of thermal comfort data collected in naturally ventilated residential buildings in fourteen cities in China. Environmental parameters, personal parameters, climatic types, and adaptive control measures were considered and used as input parameters for the ML model. It was found that environmental parameters (both outdoors and indoors), personal parameters (metabolic rate and clothing insulation), and climatic types all significant affect both TCV and TSV, while adaptive control measures only affect TSV but not TCV. Comparing with established models (PMV, ePMV and aPMV), the ML models had smaller errors in predicting TSV and TCV. Based on these results, we suggest that the ML, especially ANNs model was reliable in predicting occupants' TCV and TSV in naturally ventilated residential buildings, and performed better than traditional thermal balance based models. Moreover, using the new model, we found that acceptable temperature ranges in naturally ventilated buildings were far wider than the ASHRAE adaptive comfort zone, suggesting that appropriate models should be established based on local data. [ABSTRACT FROM AUTHOR]

We developed a tree-ring width chronology from pine trees ( Pinus tabulaeformis and Pinus armandii) stand near the peaks of Huashan, Shaanxi, north-central China. Growth-climate response analyses showed that the radial growth of pine trees is mainly influenced by April-June precipitation. A model to reconstruct precipitation based on tree widths was constructed, accounting for 55 % of the instrumental variance during the period 1953-2012. Spatial correlation analyses between the reconstruction and observed gridded precipitation data shows that the seasonal precipitation reconstruction captures regional climatic variations over north China. Compared with the historical archives and other tree-ring records in north China, many large-scale drought events, linked to the El Niño-Southern Oscillation (ENSO), were found. Many of these events have had profound impacts on the people of north China over the past several centuries. Composite maps of sea surface temperatures and 500 hPa geopotential heights for selected extremely dry and wet years in Huashan show characteristics similar to those related to the ENSO patterns, particularly with regard to ocean and atmospheric conditions in the equatorial and north Pacific. Our 531-year precipitation reconstruction for Huashan provides a long-term perspective on current and 20th century wet and dry events in north China, and is useful to guide expectations of future variability, and helps us to address climate change. [ABSTRACT FROM AUTHOR]