Quantifying fire-specific smoke exposure and health impacts.

Rapidly changing wildfire regimes across the Western United States have driven more frequent and severe wildfires, resulting in wide-ranging societal threats from wildfires and wildfire-generated smoke. However, common measures of fire severity focus on what is burned, disregarding the societal impacts of smoke generated from each fire. We combine satellite-derived fire scars, air parcel trajectories from individual fires, and predicted smoke PM2.5 to link source fires to resulting smoke PM2.5 and health impacts experienced by populations in the contiguous United States from April 2006 to 2020. We quantify fire-specific accumulated smoke exposure based on the cumulative population exposed to smoke PM2.5 over the duration of a fire and estimate excess asthma-related emergency department (ED) visits as a result of this exposure. We find that excess asthma visits attributable to each fire are only moderately correlated with common measures of wildfire severity, including burned area, structures destroyed, and suppression cost. Additionally, while recent California fires contributed nearly half of the country's smoke-related excess asthma ED visits during our study period, the most severe individual fire was the 2007 Bugaboo fire in the Southeast. We estimate that a majority of smoke PM2.5 comes from sources outside the local jurisdictions where the smoke is experienced, with 87% coming from fires in other counties and 60% from fires in other states. Our approach could enable broad-scale assessment of whether specific fire characteristics affect smoke toxicity or impact, inform cost-effectiveness assessments for allocation of suppression resources, and help clarify the growing transboundary nature of local air quality.

Exposures and behavioural responses to wildfire smoke.

Pollution from wildfires constitutes a growing source of poor air quality globally. To protect health, governments largely rely on citizens to limit their own wildfire smoke exposures, but the effectiveness of this strategy is hard to observe. Using data from private pollution sensors, cell phones, social media posts and internet search activity, we find that during large wildfire smoke events, individuals in wealthy locations increasingly search for information about air quality and health protection, stay at home more and are unhappier. Residents of lower-income neighbourhoods exhibit similar patterns in searches for air quality information but not for health protection, spend less time at home and have more muted sentiment responses. During smoke events, indoor particulate matter (PM2.5) concentrations often remain 3-4× above health-based guidelines and vary by 20× between neighbouring households. Our results suggest that policy reliance on self-protection to mitigate smoke health risks will have modest and unequal benefits.

The distribution of COVID-19-related risks.

We document two COVID-19-related risks, viral risk and employment risk, and their distributions across the Canadian population. The measurement of viral risk is based on the VSE COVID-19 Risk/Reward Assessment Tool, created to assist policy-makers in determining the impacts of pandemic-related economic shutdowns and re-openings. Women are more concentrated in high-viral-transmission-risk occupations, which is the source of their greater employment loss over the first part of the pandemic. They were also less likely to maintain contact with their former employers, reducing employment recovery rates. Low-educated workers face the same viral risk rates as high-educated workers but much higher employment losses. This is largely due to their lower likelihood of switching to working from home. For both women and the low-educated, existing inequities in their occupational distributions and living situations have resulted in them bearing a disproportionate amount of the risk emerging from the pandemic. Assortative matching in couples has tended to exacerbate risk inequities.

Dans cet article, nous documentons deux risques associés à la COVID­19, soit le risque de contracter le virus étant donné l'emploi occupé et le risque de perdre son emploi dans le contexte de la pandémie. La répartition de ces risques dans la population canadienne est aussi documentée. La mesure du risque viral est basée sur l'outil de visualisation des risques par profession et industrie liés à la COVID­19 de la VSE, créée pour aider les décideurs à déterminer les impacts des fermetures et réouvertures des différents secteurs de l'économie durant la pandémie. On note que les femmes sont plus présentes dans les professions à haut risque viral, ce qui explique en partie leur plus grande perte d'emploi durant la première partie de la pandémie. Durant la pandémie, elles étaient également moins susceptibles de demeurer en contact avec leurs anciens employeurs, ce qui a affecté négativement leur taux de retour au travail. Le risque viral était similaire pour les travailleurs peu éduqués et les travailleurs hautement qualifiés, mais les pertes d'emplois ont été beaucoup plus importantes pour les travailleurs peu éduqués. Cette différence peut être attribuable à leur plus faible capacité à effectuer leur travail à domicile étant donné la nature de leur emploi. Tant pour les femmes que pour les personnes peu éduquées, les inégalités existantes dans leurs conditions de vie et leur répartition professionnelle les ont conduites à subir une part plus élevée du risque lié à la pandémie. Enfin, l'appariement assortatif des couples selon les professions a eu tendance à exacerber les inégalités face aux risques.

Rapidly declining remarkability of temperature anomalies may obscure public perception of climate change.

The changing global climate is producing increasingly unusual weather relative to preindustrial conditions. In an absolute sense, these changing conditions constitute direct evidence of anthropogenic climate change. However, human evaluation of weather as either normal or abnormal will also be influenced by a range of factors including expectations, memory limitations, and cognitive biases. Here we show that experience of weather in recent years-rather than longer historical periods-determines the climatic baseline against which current weather is evaluated, potentially obscuring public recognition of anthropogenic climate change. We employ variation in decadal trends in temperature at weekly and county resolution over the continental United States, combined with discussion of the weather drawn from over 2 billion social media posts. These data indicate that the remarkability of particular temperatures changes rapidly with repeated exposure. Using sentiment analysis tools, we provide evidence for a "boiling frog" effect: The declining noteworthiness of historically extreme temperatures is not accompanied by a decline in the negative sentiment that they induce, indicating that social normalization of extreme conditions rather than adaptation is driving these results. Using climate model projections we show that, despite large increases in absolute temperature, anomalies relative to our empirically estimated shifting baseline are small and not clearly distinguishable from zero throughout the 21st century.

Weather impacts expressed sentiment.

We conduct the largest ever investigation into the relationship between meteorological conditions and the sentiment of human expressions. To do this, we employ over three and a half billion social media posts from tens of millions of individuals from both Facebook and Twitter between 2009 and 2016. We find that cold temperatures, hot temperatures, precipitation, narrower daily temperature ranges, humidity, and cloud cover are all associated with worsened expressions of sentiment, even when excluding weather-related posts. We compare the magnitude of our estimates with the effect sizes associated with notable historical events occurring within our data.

Climate change is projected to have severe impacts on the frequency and intensity of peak electricity demand across the United States.

It has been suggested that climate change impacts on the electric sector will account for the majority of global economic damages by the end of the current century and beyond [Rose S, et al. (2014) Understanding the Social Cost of Carbon: A Technical Assessment]. The empirical literature has shown significant increases in climate-driven impacts on overall consumption, yet has not focused on the cost implications of the increased intensity and frequency of extreme events driving peak demand, which is the highest load observed in a period. We use comprehensive, high-frequency data at the level of load balancing authorities to parameterize the relationship between average or peak electricity demand and temperature for a major economy. Using statistical models, we analyze multiyear data from 166 load balancing authorities in the United States. We couple the estimated temperature response functions for total daily consumption and daily peak load with 18 downscaled global climate models (GCMs) to simulate climate change-driven impacts on both outcomes. We show moderate and heterogeneous changes in consumption, with an average increase of 2.8% by end of century. The results of our peak load simulations, however, suggest significant increases in the intensity and frequency of peak events throughout the United States, assuming today's technology and electricity market fundamentals. As the electricity grid is built to endure maximum load, our findings have significant implications for the construction of costly peak generating capacity, suggesting additional peak capacity costs of up to 180 billion dollars by the end of the century under business-as-usual.