Toward Systemic Thinking in Managing Environmental Risks

1.Introduction The world today is strongly interconnected.Numerous interdependent and complex networks have been formed between environmental,economic,and social systems,through which people,resources,materials,goods,and information are exchanged at unprecedented speeds[1].At the same time,however,such networks are profoundly changing the global risk landscape and making the whole system more vulnerable[2].In particular,there is a growing concern about cascading and systemic risks.In such cases,a localized initial damaging event(e.g.,the coronavirus disease 2019(COVID-19)pandemic)can spread rapidly and globally,resulting in disruptive influences and countless societal costs[3].

Clean air captures attention whereas pollution distracts:evidence from brain activities

Awareness of the adverse impact of air pollution on attention-related performance such as learning and driving is rapidly growing.However,there is still little known about the underlying neurocognitive mechanisms.Using an adapted dot-probe task paradigm and event-related potential(ERP)technique,we investigated how visual stimuli of air pollution influence the attentional allocation process.Participants were required to make responses to the onset of a target presented at the left or right visual field.The probable location of the target was forewarned by a cue(pollution or clean air images),appearing at either the target location(attention-holding trials)or the opposite location(attention-shifting trials).Behavioral measures showed that when cued by pollution images,subjects had higher response accuracy in attention-shifting trials.ERP analysis results revealed that after the cue onset,pollution images evoked lower N300 amplitudes,indicating less attentioncapturing effects of dirty air.After the target onset,pollution cues were correlated with the higher P300 amplitudes in attention-holding trials but lower amplitudes in attention-shifting trials.It indicates that after visual exposure to air pollution,people need more neurocognitive resources to maintain attention but less effort to shift attention away.The findings provide the first neuroscientific evidence for the distracting effect of air pollution.We conclude with several practical implications and suggest the ERP technique as a promising tool to understand human responses to environmental stressors.

An ensemble machine learning model to uncover potential sites of hazardous waste illegal dumping based on limited supervision experience

With the soaring generation of hazardous waste(HW)during industrialization and urbanization,HW illegal dumping continues to be an intractable global issue.Particularly in developing regions with lax regulations,it has become a major source of soil and groundwater contamination.One dominant challenge for HW illegal dumping supervision is the invisibility of dumping sites,which makes HW illegal dumping difficult to be found,thereby causing a long-term adverse impact on the environment.How to utilize the limited historic supervision records to screen the potential dumping sites in the whole region is a key challenge to be addressed.In this study,a novel machine learning model based on the positive-unlabeled(PU)learning algorithm was proposed to resolve this problem through the ensemble method which could iteratively mine the features of limited historic cases.Validation of the random forest-based PU model showed that the predicted top 30%of high-risk areas could cover 68.1%of newly reported cases in the studied region,indicating the reliability of the model prediction.This novel framework will also be promising in other environmental management scenarios to deal with numerous unknown samples based on limited prior experience.

Same stimuli,different responses:a pilot study assessing air pollution visibility impacts on emotional well-being in a controlled environment

A growing number of studies have shown that impaired visibility caused by particulate matter pollution influences emotional wellbeing.However,evidence is still scant on how this effect varies across individuals and over repetitive visual exposure in a controlled environment.Herein,we designed a lab-based experiment(41 subjects,6 blocks)where participants were presented with real-scene images of 12 different PM_(2.5) concentrations in each block.Emotional valence(negative to positive)and arousal(calm to excited)were self-rated by participants per image,and the response time for each rating was recorded.We find that as pollution level increases from 10 to 260µg/m3,valence scores decrease,whereas arousal scores decline first and then bounce back,following a U-shaped trend.When air quality deteriorates,individual variability decreases in hedonic valence but increases in arousal.Over blocks,repetitive visual exposure increases valence at a moderate pollution level but aggravates negative emotions in severely polluted conditions(>150µg/m3).Finally,we find females,people who are slow in making responses,and those who are highly aroused by clean air tend to express more negative responses(so-called negativity bias)to ambient pollution than their respective counterparts.These results provide deeper insights into individual-level emotional responses to dirty air in a controlled environment.Although the findings in our pilot study should only be directly applied to the conditions assessed herein,we introduce a framework that can be replicated in different regions to assess the impact of air pollution on local emotional wellbeing.

Leveraging individual-level data to advance air pollution health risk management

1 Introduction Environmental health risk management is a systematic engineering task,engaging multiple disciplines from the academic and government sectors.Reducing environmental health risks has become one of the key targets in the United Nations Sustainable Development Goals(SDGs).This target has been translated into public policies at many jurisdictional levels(Yue et al.,2020).To design region-specific and targeted policy initiatives,understanding how environmental health risks are spatially distributed and temporally resolved is fundamental.