We observe causal relationships naturally and quickly in events that we experience in our life. The current research investigates if causal events like collisions attract our attention to other changes in objects involved in the causal event. Participants reported colour changes in two objects, one involved in a causal event (collision) and the other independent. Aligning with our expectation, we observed that participants are more likely to report the colour change involved in the causal event when it happened at the same time as the collision. Against our prediction however, we observed a similar effect when colour changes happened before the collision, while the difference was less strong when the colour changes happened after the collision. One possible explanation is that the effect stems from participants anticipating causal events, leading them to pay extra attention to objects potentially involved in collisions. This focused attention makes participants more likely to notice colour changes during the anticipation period, which means people are actively devoting more cognitive resources anticipating and confirming causal interactions. This finding suggests that people prioritise causal observations in visual search tasks.

We introduce a novel methodology to scrutinize blame attributions in 'Tweets', focusing on Artificial Intelligence (AI) incidents - a contemporary issue that provokes regular discourse. The method identifies the agents that get blamed and the factors that are associated with blame attributions. The proposed methodology replicates and contextualizes findings from experimental settings, revealing AI entities are often held accountable for adverse outcomes, while human agents are judged based on intentions. It also identifies unexplored factors, such as blaming data for perceived biases or AI for replacing humans. This method offers a robust tool for mitigating measurement bias in specific fields, enabling the continual rejuvenation of theoretical frameworks with emerging variables.

Alkyl nitrates (RONO₂) are important reservoirs of nitrogen oxides and play key roles in the tropospheric chemistry. Two phases of intensive campaigns were conducted during February-April and June-July of 2017 at a rural coastal site and in open oil fields of the Yellow River Delta region, northern China. C₁-C₅ alkyl nitrates showed higher concentration levels in summer than in winter-spring (p < 0.01), whilst their parent hydrocarbons showed an opposite seasonal variation pattern. The C₃-C₅ RONO₂ levels in the oil fields were significantly higher than those in the ambient rural air. Alkyl nitrates showed well-defined diurnal variations, elucidating the effects of in-situ photochemical production and regional transport of aged polluted plumes. Backward trajectory analysis and fire maps revealed the significant contribution of biomass burning to the observed alkyl nitrates and hydrocarbons. A simplified sequential reaction model and an observation-based chemical box model were deployed to diagnose the formation mechanisms of C₁-C₅ RONO₂. The C₃-C₅ RONO₂ were mainly produced from the photochemical oxidation of their parent hydrocarbons (i.e., C₃-C₅ alkanes), whilst C₁-C₂ RONO₂ compounds have additional sources. In addition to parent hydrocarbons, longer alkanes with >4 carbon atoms were also important precursors of alkyl nitrates in the oil fields. This study demonstrates the significant effects of oil field emissions and biomass burning on the volatile organic compounds and alkyl nitrate formation, and provides scientific support for the formulation of control strategies against photochemical air pollution in the Yellow River Delta region.