Affective Sensitivity to Air Pollution (ASAP): Person-specific associations between daily air pollution and affective states

Michelle Ng; Denis Gerstorf; David E. Conroy; Aaron L. Pincus; Nilàm Ram

doi:10.1371/journal.pone.0307430

Abstract

Individuals’ sensitivity to climate hazards is a central component of their vulnerability to climate change. In this paper, we introduce and outline the utility of a new intraindividual variability construct, affective sensitivity to air pollution (ASAP)–defined as the extent to which an individual’s affective states fluctuate in accordance with daily changes in air quality. As such, ASAP pushes beyond examination of differences in individuals’ exposures to air pollution to examination of differences in individuals’ sensitivities to air pollution. Building on known associations between air pollution exposure and adverse mental health outcomes, we empirically illustrate how application of Bayesian multilevel models to intensive repeated measures data obtained in an experience sampling study (N = 150) over one year can be used to examine whether and how individuals’ daily affective states fluctuate with the daily concentrations of outdoor air pollution in their county. Results indicate construct viability, as we found substantial interindividual differences in ASAP for both affect arousal and affect valence. This suggests that repeated measures of individuals’ day-to-day affect provides a new way of measuring their sensitivity to climate change. In addition to contributing to discourse around climate vulnerability, the intraindividual variability construct and methodology proposed here can help better integrate affect and mental health in climate adaptation policies, plans, and programs.

Citation: Ng M, Gerstorf D, Conroy DE, Pincus AL, Ram N (2024) Affective Sensitivity to Air Pollution (ASAP): Person-specific associations between daily air pollution and affective states. PLoS ONE 19(8): e0307430. https://doi.org/10.1371/journal.pone.0307430

Editor: Amaal Gh. Yasser, Griffith University, AUSTRALIA

Received: December 4, 2023; Accepted: July 4, 2024; Published: August 7, 2024

Copyright: © 2024 Ng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files.

Funding: This work was generously supported by the US National Institute on Aging (RC1-AG035645) and the US National Science Foundation Graduate Research Fellowship Program (DGE-1656518).

Competing interests: The authors have declared that no competing interests exist.

Introduction

Over the past two decades, conceptual thinking around vulnerability to climate change has undergone a paradigm shift [1–3]. In the early 2000s, the Intergovernmental Panel on Climate Change (IPCC) defined climate vulnerability as a function of one’s exposure, sensitivity, and adaptive capacity to climate change [4]. Under this older paradigm, where exposure and sensitivity to climate change were treated as “almost inseparable properties of a system” [5], measures of exposure were often used as proxy indicators of sensitivity. The two constructs were viewed as so coupled that measuring one was good enough. More recently, however, the IPCC decoupled sensitivity from exposure. Climate vulnerability was redefined as a function of one’s sensitivity and adaptive capacity to climate change [6, 7]–existing within the context of, but not identical to, one’s exposure. Under this newer paradigm, sensitivity to climate change should no longer be proxied by exposure to climate change [3]. The new approach raises the question: How might we measure sensitivity to climate change?

Following the IPCC’s conceptual shift in defining climate vulnerability, we propose that intensive longitudinal data on individuals’ daily affect obtained in experience sampling studies can be used to measure sensitivity to climate change. Recognizing that climate vulnerability is largely produced by power relations and other socio-political processes [8, 9], we view psychological sensitivity to climate change as one indicator within a larger, complex system of individuals’ and communities’ overall sensitivity to climate change. We focus on affect as a measure of sensitivity for three reasons. First, affect is a reliable, sensitive, and time-varying marker of individuals’ responses to their environments [10]. Individuals’ affective states change from moment to moment and from day to day—continually providing information about how they are doing, whether there are threats in their environments, and whether they should approach or avoid those threats (e.g., anger, sadness). Studying day-to-day fluctuations in individuals’ affective states reveals if and how individuals are influenced by changes in their everyday environments. For example, systematic increases in individuals’ daily negative affect that are coupled with daily stressors indicate the extent to which individuals are reactive to those stressors [11]. There are also interindividual differences in affective reactivity, such that different individuals respond differently to changes in their environment [12]. For instance, individuals who are higher in neuroticism tend to have greater reactivity to daily stressors [13–15]; and older adults may experience less pronounced fluctuations in their affect compared to younger adults [16, 17]. Examining how individuals’ affect fluctuates in accordance with changes in air pollution can illuminate whether and how air pollution exposure is experienced differently across individuals—thus providing a new way to measure sensitivity to climate change.

Second, affect is a defining characteristic of aspects of mental health that are associated with both climate change and air pollution exposure. In its most recent assessment report, the IPCC [7] states with “very great confidence” that climate change adversely influences mental health through numerous pathways; and as detailed below, prior research has established linkages between mental health and air pollution exposure specifically. Given that disturbed affect and affect regulation play a role in 40–75% of mental disorders [18–21], it is likely that affect is a mechanism through which air pollution exposures influence mental health. Examining how individuals’ affect fluctuates in accordance with daily changes in air pollution can provide new insight into whether and how day-to-day affective dynamics induced by air pollution contribute to changes in or differences between individuals’ mental health and wellbeing.

Third, individuals’ affect is a key driver of their climate change mitigation behaviors, such as recycling and conserving energy, and adaptation behaviors, such as purchasing insurance and evacuating. Various theories, such as the Extended Parallel Process Model and Climate Change Risk Perception Model, postulate that affect informs behavioral decision-making [22, 23]. Accordingly, empirical evidence shows that negative affect is the largest predictor of individuals’ climate risk perception [23] and willingness to engage in climate mitigation behaviors [24], as well as one of the largest predictors of individuals’ climate adaptation behaviors [25, 26]. Put differently, affect is a cornerstone of climate action. If air pollution blunts individuals’ affect in some way, individuals may perform fewer mitigation and adaptation behaviors—with implications for climate change and their personal wellbeing [27]. Examining how an individual’s affect fluctuates in accordance with daily changes in air pollution thus illuminates another pathway by which an individual’s exposure to climate hazards may indirectly influence their climate actions.

Air pollution and wellbeing

The intensification of air pollution due to climate change (e.g., particulate matter from wildfire smoke, ozone from hotter temperatures) is making air an increasingly visible part of many individuals’ daily experiences [28–31]. Already 90% of the population worldwide, especially communities of color and low-income communities, breathes air that does not meet the World Health Organization’s guidelines for livable air quality [32–34]. This is dangerous because exposure to carbon monoxide (CO), nitrogen oxides (NO_x), ozone (O₃), sulfur dioxide (SO₂), and particulate matter (PM_2.5 and PM₁₀) is associated with a variety of health problems, including premature mortality [35], cardiovascular disease and serious cardiac events [36], asthma attacks [37], and lung cancer [38].

Exposure to air pollution is also associated with a variety of psychological states and disorders, including higher levels of perceived stress in older men [39], higher stress responsivity among adolescent girls at risk for anxiety disorders [40], increased odds of depressive and anxiety symptoms in older adults [41], increased risk of emergency department visits for youth with diagnosed mental illnesses [42], and the development of psychopathology in 18-year-olds who had been exposed to air pollution as children and adolescents [43]. The links between mental health and long-term exposure to air pollution suggest that fluctuations in affect—a core element of mental health—may reflect the short-term effects of air pollution on individuals’ wellbeing. In addition, individuals’ affect to be sensitive to daily changes in air pollution because affect-driving health behaviors, such as physical activity and high-quality sleep, are compromised by air pollution [44–50]. Given these links, the extent to which an individual’s daily affective states fluctuate in accordance with their daily exposures to air pollution should provide a new way to measure sensitivity to climate change.

Affective Sensitivity to Air Pollution (ASAP)

Building from prior research on individuals’ psychological sensitivity to changes in their everyday environments and life experiences [15, 17, 51, 52], we propose using a new intraindividual variability construct, affective sensitivity to air pollution (ASAP), to measure psychological sensitivity to climate change. Formally, ASAP is defined as the extent to which an individual’s affective states fluctuate in accordance with daily changes in air pollution. ASAP is measured using experience sampling methodologies, wherein the reports individuals provide every few hours about their psychological states (e.g., ecological momentary assessments) are combined with environmental data obtained from local air quality monitors. As illustrated in Fig 1, individuals with higher ASAP experience greater fluctuations in their affective states in accordance with daily changes in air pollution compared to individuals with lower ASAP.

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Fig 1. Intraindividual variability in air pollution and affect arousal for three individuals with different levels of affective sensitivity to air pollution (ASAP).

Fluctuations in air pollution (blue line) and affect arousal (orange line) over time (x-axis) are shown for three individuals (rows). The correlation coefficient, r, indicates the strength of covariation for each individual’s air pollution and affect arousal.

https://doi.org/10.1371/journal.pone.0307430.g001

In this paper, we explore whether and how individuals’ daily affective states fluctuate in accordance with their daily exposures to air pollution. Our goals are twofold: (1) to introduce the concept of affective sensitivity to air pollution (ASAP) and (2) to illustrate how ASAP can be measured using available empirical data. Using Bayesian multilevel models that isolate within-person covariation, we demonstrate how intensive longitudinal data enable examination of the prototypical individual’s ASAP, interindividual differences in ASAP, and whether interindividual differences in ASAP are related to overall levels of chronic exposure to air pollution. We conclude with a discussion of how measurement of ASAP—as one form of sensitivity to climate change—can inform climate adaptation policies, plans, and programs. While our proposal and empirical illustration focus on individuals’ ASAP, they straightforwardly transfer to assessments of any climate hazard and outcome variable of interest.

Materials and method

Our empirical illustration of individuals’ ASAP combines two data sources. Data on individuals’ daily affective states were obtained in the Intraindividual Study of Affect, Health, and Interpersonal Behavior (iSAHIB), an intensive longitudinal study that collected rich repeated measures of its namesake variables in everyday life [53]. Data on daily levels of air pollution were obtained from the United States Environmental Protection Agency’s (EPA) compilation of air pollution data from outdoor monitors located across the United States [54]. Data from iSAHIB has previously been used to examine intraindividual associations between physical activity and life satisfaction [55], alcohol use and sleep quality [56], interpersonal behavior and personality [57], and social interactions and core affect [58]. Here, these daily affect data are for the first time combined with environmental (in this case, air pollution) data.

Participants & procedure

The iSAHIB study protocol (#33706) was approved by the Institutional Review Board of Pennsylvania State University. Written consent was obtained from participants. The iSAHIB sample consisted of 150 adults (51% women) living around central Pennsylvania, USA, specifically in Centre County (n = 134), nearby counties in Pennsylvania (n = 3 each in Blair, Clearfield, and Clinton Counties; n = 1 each in Cumberland, Lehigh, Lycoming, Mifflin, and Montgomery Counties), and nearby counties in Maryland and New Jersey (n = 1 each in Carrol and Somerset Counties). Participants were between 18 and 89 years of age (M_age = 47.10, SD_Age = 18.76) and had obtained between 2 and 24 years of formal education (M_Educ = 16.36, SD_Educ = 3.90). Half were employed full-time; 64% were engaged, married, or in committed relationship (15% single); 79% lived with family (15% with roommates, etc., 6% alone); 91% self-identified as White (4% African American, 1% Asian American, and 4% multiracial or some other race); and 93% as heterosexual.

After being recruited between April 23, 2010 and April 18, 2011, informed about the intensive assessment protocol, and providing written informed consent, individuals contributed extensive reports about their lives during the next year through a combination of web-based (completed during visits to the laboratory) and smartphone-based (completed multiple times per day during daily life) questionnaires. Most relevant for our illustration of ASAP, participants responded to daily surveys (52 items) on their study-provided smartphone each evening for three 21-day measurement bursts (9 weeks total) over the course of one year (about 4.5-month intervals between bursts) between May 2010 and July 2011. Of the 150 participants, 136 (90.7%) completed the entire intensive protocol, 11 (7.3%) completed one-third of the protocol, and 3 (2.0%) completed two-thirds of the protocol. Participants who withdrew after partial completion did not differ systematically from those who completed the entire protocol with respect to the measured demographics (ps >.05). Some participants also completed extra days until they returned the phone and formally exited the study. Overall, participants provided 8,541 daily reports of affect. This analysis makes use of 8,250 (96.6%) of these daily reports that could be linked to daily air pollution data (M = 56.5 daily reports; SD = 13.0; range 13 to 76).

Measures

Daily air pollution.

Individuals’ daily exposure to air pollution, specific to the county in which they lived, was measured using the United States’ Air Quality Index (AQI). The AQI is calculated and issued each day by the EPA based on concentrations of the five major pollutants regulated by the Clean Air Act: ground-level ozone, particulate matter, carbon monoxide, sulfur dioxide, and nitrogen dioxide [59]. The AQI value for a given day is the maximum concentration observed across the five pollutants (each scaled such that 100 is the national public health air quality standard for that pollutant). On a scale from 0 to 500, the AQI communicates the daily level of air pollution and its associated health concern: 0 to 50 (“Good”), 51 to 100 (“Moderate”), 101 to 150 (“Unhealthy for Sensitive Groups”), 151 to 200 (“Unhealthy”), 201 to 300 (“Very Unhealthy”), and 301 and higher (“Hazardous).

County-level AQI data for the dates of iSAHIB data collection were downloaded from the EPA’s data repository [54]. Two counties did not have any available AQI data, four counties had AQI data based on one major pollutant, two counties had AQI data based on two major pollutants, two counties had AQI data based on three major pollutants, and one county had AQI data based on four major pollutants. Due to these gaps, AQI was available for 96.6% of the daily reports obtained across iSAHIB (8,250 of 8,541 person-days). Following the available AQI data, we focused our analysis on daily reports that could be paired with AQI: 97.3% of the daily reports obtained in Burst 1 of iSAHIB (2,972 of 3,056 person-days); 95.6% of Burst 2 (2,672 of 2,795 person-days); and 96.9% of Burst 3 (2,606 of 2,690 person-days). Examination of missingness patterns suggested that the missing AQI data could reasonably be treated as missing at random.

Daily affect (arousal and valence).

In all three bursts of the iSAHIB, individuals’ daily affective states were measured at the end of each day by asking participants to indicate how strongly they felt 20 emotions. Participants responded to items such as “Today I felt [HAPPY]” using a slider-type interface with end-point anchors labeled “Not at all…Strongly” that was digitally coded on a 0 to 100 scale (numbers not visible to participants). Even distribution of the 20 emotion-specific items across all four quadrants of the core affect circumplex [60] facilitated calculation of daily affect arousal, which describes the level of physiological activation involved in an individual’s affective state, and daily affect valence, a hedonistic evaluation describing the positivity or negativity of an individual’s affective state. Specifically, following Kuppens et al. [61], arousal on day t for person i was calculated as, (1a) and valence on day t for person i was calculated as, (1b) where HAP_ti is the mean of person i’s reports on day t for the five high arousal positive emotions: alert, happy, enthusiastic, excited, and proud; HAN_ti is the mean of person i’s reports on day t for the five high arousal negative emotions: embarrassed, nervous, stressed, tense, and upset; LAP_ti is the mean of person i’s reports on day t for the five low arousal positive emotions: calm, content, peaceful, relaxed, and satisfied; and LAN_ti is the mean of person i’s reports on day t for the five low arousal negative emotions: bored, depressed, disappointed, sad, and sluggish. For convenience of interpretation, resulting scores for both arousal and valence were divided by 2 so they range from -100 to 100.

Day.

To control for systematic linear time trends in daily affect that may have occurred over the course of the 9-week study period, Day in study was included as a covariate.

Data configuration and analysis

As outlined above, ASAP is defined as the extent to which fluctuations in an individual’s affect are related to fluctuations in air pollution levels—that is, the intraindividual covariation of daily air pollution (AQI) scores and daily affect scores. Illustrative plots of three individuals’ daily AQI and affect arousal are shown in Fig 1. For the participant represented in the top panel, fluctuations in AQI and levels of arousal do not appear associated; while for the participants represented in the middle and bottom panels, fluctuations in AQI and levels of arousal are systematically linked in that higher levels of AQI tend to coincide with lower levels of arousal. Interindividual differences in the strength of ASAP indicated by differences in the extent of covariation (e.g., r = -0.01 versus r = -0.30 versus r = -0.46).

Formally, individuals’ ASAP and interindividual differences in ASAP were examined in a Bayesian multilevel modeling framework [62] that accommodated the nested nature of the intensive longitudinal data (daily repeated measures nested within persons) and enabled robust inference. Following usual practice in analysis of intensive longitudinal data [63], the daily AQI reports were separated into within-person and between-person components prior to analysis. Specifically, overall air pollution_i scores were calculated for each participant as the within-person mean of all their repeated measures of AQI. Daily air pollution_ti scores were then calculated as that day’s deviation from the individual’s overall air pollution_i score. The within- and between-person associations of daily affect and air pollution were then examined using 2-level models of the form, (2) where the repeated measures of daily affect (valence and arousal examined in separate models) for day t for individual i, Affect_ti, is modeled as a function of person-specific intercepts, β_0i, that indicate an individual’s baseline level of affect; person-specific slope coefficients, β_1i, that indicate an individual’s ASAP; person-specific time-related trends, β_2i; and residual error, e_ti, that is assumed normally distributed with standard deviation σ_e. Person-specific coefficients were simultaneously modeled as a function of the person-level exposure variables, (3) (4) (5) where the γs are sample-level parameters that describe the prototypical levels of baseline affect and ASAP and how between-person differences in baseline affect and ASAP are associated with differences in overall level of exposure to air pollution, and the us are residual unexplained between-person differences that are assumed multivariate normally distributed with standard deviations σ_u0 and σ_u1, and correlation r_u0u1. Person-level predictors were sample-mean centered. In line with our treatment of Day_ti as a nuisance covariate, random effects for the linear time trends were not included. Model explorations indicated no substantial differences in the pattern of findings across a variety of alternative random effects structures.

We expect that for the prototypical person, days with higher air pollution exposure will be accompanied by changes in affect arousal and affect valence (γ₁₀). Previously noted associations between air pollution exposure and depressive symptoms suggest that on days when air pollution is higher, affect arousal will be lower and affect valence will be lower. However, previously noted associations between air pollution exposure and anxiety symptoms suggest that on days when air pollution is higher, affect arousal will be higher. Similarly mixed findings around habituation versus sensitization suggest that chronic exposure to air pollution will impact ASAP (γ₁₁), but the direction of impact is unclear. When facing chronic exposure to any stimulus, individuals may exhibit greater affective sensitivity (sensitization) or reduced affective sensitivity (habituation) [52]. Past research has suggested that affect regulation skills may be acquired over multiple exposures to a stimulus, as individuals learn how to adapt [17, 64]. Following this logic, we can expect that, over the long-term, individuals become more or less sensitive to changes in air pollution through sensitization or habituation processes, such that differences in overall exposure moderate extent of ASAP. Overall, our analysis extends prior research linking air pollution exposures and adverse mental health outcomes by examining individuals’ ASAP at a daily time-scale.

The multilevel models were estimated in a Bayesian analysis framework using the brms package in R [65]. Estimation was based on two chains of 2,000 iterations (500 of which were warm-up) that provided a total of 3,000 samples for the posterior distributions. We used the mildly informative default priors, a half Student’s-t prior with 3 degrees of freedom and scale parameters between 8.9 to 26.7 (depending upon outcome variable) for standard deviations of random effects, a Lewandowski—Kurowicka—Joe prior with parameter of 1 for correlations among random effects, and flat or Student’s-t priors with mildly informative means for regression coefficients. Incomplete data were treated using standard missing at random assumptions. Convergence of the Markov Chain Monte Carlo (MCMC) algorithms was determined through graphical checks of the chains and posterior distributions, inspection of R-hat values, and posterior predictive checks, all of which suggested that MCMC chains had converged. In follow-up sensitivity analyses, we examined subsets of the emotions (i.e., positive high arousal emotions, positive low arousal emotions, negative high arousal emotions, and negative low arousal emotions). The same general pattern of results reported in the main analysis also manifested in the sensitivity analyses (see S1 Table). Following the Bayesian estimation, substantive inferences are obtained from examination of 95% credible intervals and probability of direction (which is interpreted similarly to a p-value) obtained from the posterior distributions of each model parameter.

Results

We examined the strength of ASAP for different individuals, the direction of ASAP, and whether interindividual differences in ASAP are related to overall exposure to air pollution. Separate models were used to examine sensitivity of affect arousal and affect valence. Descriptive statistics are included in Table 1.

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Table 1. Descriptive statistics for air pollution and two characteristics of affective states: Arousal and valence.

https://doi.org/10.1371/journal.pone.0307430.t001

Results for models examining how daily air pollution is associated with fluctuations in affect arousal and affect valence are included in Table 2 and illustrated in Fig 2.

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Fig 2. Within-person and between-person associations for affect and air pollution.

Panel A: Within-person associations of daily air pollution and daily arousal. Panel B: Between-person associations of overall air pollution and overall arousal. Panel C: Within-person associations of daily air pollution and daily valence. Panel D: Between-person associations of overall air pollution and overall valence. In Panels A and C, the thin black lines show interindividual differences in affective sensitivity to air pollution (ASAP), while the thick blue line represents the prototypical individual’s ASAP.

https://doi.org/10.1371/journal.pone.0307430.g002

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Table 2. Results from multilevel models for affect arousal and affect valence.

https://doi.org/10.1371/journal.pone.0307430.t002

Affect arousal

As seen in the first model in Table 2 and displayed in the top panels of Fig 2, the prototypical individual’s baseline level of arousal was γ₀₀ = –0.89 (probability of direction, or pd = 93.50%) and increased slightly across days, γ₂₀ = 0.03 (pd = 100%). In line with hypotheses, the prototypical individual’s ASAP manifesting in arousal was γ₁₀ = –0.01 (pd = 97.83%). That is, on days when air pollution is higher than usual by one unit on the 0 to 500 scale, the prototypical individual’s level of arousal is –0.01 unit lower on the –100 to 100 scale.

Paralleling the prototypical within-person association captured through articulation of ASAP, there was only weak evidence that individuals with greater overall exposure to air pollution had lower baseline levels of arousal, γ₀₁ = –0.10 (pd = 83.30%). However, in line with hypotheses, there was stronger evidence that individuals with greater overall exposure to air pollution may have marginally less pronounced ASAP, γ₁₁ = 0.002 (pd = 97.17%), suggesting habituation.

Most important for our empirical illustration of the ASAP construct, there were, even after controlling for differences in overall exposure to air pollution, substantial interindividual differences in ASAP, σ_u1 = 0.01 (pd = 100%), that were not correlated (r_u0u1 = –0.07, pd = 57.77%) with the typically expected differences in individuals’ baseline levels of arousal, σ_u0 = 7.16 (pd = 100%). As displayed in the top left panel of Fig 2, the results confirm that interindividual differences in ASAP (shown by the thin black lines) can be discerned using intensive longitudinal data, and that the prototypical individual’s ASAP manifests in the expected way (negative slope of the thick blue line) for affect arousal. In other words, there are significant between-person differences in within-person sensitivity of affect arousal to daily changes in air pollution.

Affect valence

As seen in the second model in Table 2 and displayed in the bottom panels of Fig 2, the prototypical individual’s baseline level of valence was γ₀₀ = 35.03 (pd = 100%) and increased slightly across days, γ₂₀ = 0.03 (pd = 99.77%). However, there was not strong evidence that the prototypical individual’s ASAP manifesting in valence was different than zero (γ₁₀ = 0.02; pd = 92.40%).

Paralleling the prototypical within-person association captured through articulation of ASAP, there was no evidence that individuals with greater overall exposure to air pollution had lower baseline levels of valence, γ₀₁ = 0.17 (pd = 72.60%). In line with hypotheses, there was a hint of evidence that individuals with greater overall exposure to air pollution may have less pronounced ASAP, γ₁₁ = –0.005 (pd = 94.40%), suggesting habituation.

Most important for our empirical illustration of the ASAP construct, there were, even after controlling for differences in overall exposure to air pollution, substantial interindividual differences in ASAP, σ_u1 = 0.13 (pd = 100%), that were not correlated (r_u0u1 = –0.01, pd = 55.97%) with the typically expected differences in individuals’ baseline levels of valence, σ_u0 = 21.50 (pd = 100%). As displayed in the bottom left panel of Fig 2, the results confirm that interindividual differences in ASAP (shown by the thin black lines) can be discerned using intensive longitudinal data, even though the prototypical individual’s ASAP is not different than zero (thick blue line) for affect valence. In other words, there are significant between-person differences in within-person sensitivity of affect valence to daily changes in air pollution.

Discussion

This paper introduced the ASAP construct and illustrated its measurement using intensive longitudinal data. Our empirical illustration demonstrates the viability of using air pollution data obtained from local air quality monitors and psychological data obtained in experience sampling studies to articulate individuals’ ASAP. As expected, we found that ASAP was indeed discernable, that the prototypical individual’s affect arousal was lower than usual on days with higher than usual air pollution, and that—most importantly—there were indeed substantial interindividual differences in ASAP for both affect arousal and affect valence. Results on how ASAP differed in relation to individuals’ chronic exposure to air pollution suggested habituation effects for both affect arousal and affect valence, but we maintain a cautious interpretation of that finding for reasons outlined below.

Our findings that individuals’ day-to-day affect may be disrupted by air pollution has three implications. First, ASAP could help partially explain one of the mechanisms by which exposure to air pollution increases longer-term risk for adverse mental health outcomes, like symptoms of anxiety and depression, found in prior research [39–43]. Put differently, climate change is exposing more individuals to air pollution that disturbs affect. Since disturbed affect is implicated in 40–75% of mental disorders [18–21], a more detailed understanding of ASAP can help support climate adaptation by developing strategies to safeguard mental health. Second, since affect is a key driver of individuals’ climate mitigation and adaptation behaviors [24–26], if air pollution blunts an individual’s affect, the blunting might carry over into a (lack of) climate action [27]. Lack of mitigation behaviors could substantially impede the international goal of limiting warming to 1.5 degrees Celsius above pre-industrial levels, as enshrined in the Paris Agreement [66]. Lack of adaptation behaviors could also impede the protective actions taken by individuals exposed to climate hazards. For example, if people’s affect is blunted due to wildfire-induced air pollution during the summer, they may take fewer adaptation behaviors in response to both air pollution and heat waves, with serious implications for their wellbeing. Third, although the whole sample lived in one geographic region and experienced comparable exposures to air pollution, they still presented different ASAP. Aligning with the IPCC’s [7] redefinition of climate vulnerability, these interindividual differences in ASAP underscore the distinction between sensitivity and exposure to climate change.

Applications for policy and practice

Climate adaptation is “the process of adjustment to actual or expected climate and its effects, in order to moderate harm or exploit beneficial opportunities” [67]. Leveraging the centrality of affect in psychological processes, ASAP exemplifies the type of constructs that can help us monitor and support mental health in the face of climate hazards, such as climate change-induced air pollution (e.g., wildfire smoke). First, ASAP can be used to inform climate vulnerability assessments. As assessors of climate vulnerability begin looking beyond communities’ exposure to climate hazards and toward communities’ sensitivity and adaptive capacity [1, 3–6, 8, 9], constructs like ASAP could be integrated into climate vulnerability assessments as indicators for sensitivity to climate change. It is important to note, however, that because vulnerability is a socially produced state where people are made vulnerable to climate change through systems of power, ASAP should be considered one component of a much larger system. Second, ASAP can guide policy and investment decisions by highlighting differences in sensitivity to air pollution across individuals, when the extent of these differences may otherwise be obscured in population-level data. For example, while unexamined in this study, it is possible that certain populations, such as older adults or children, exhibit higher ASAP than others. While measures of ASAP do not address the root causes of air pollution, or its inequitable distribution [33], they can help quantify the effects of air pollution where it is already being felt and direct resources accordingly. Third, ASAP can enable the design of personalized interventions that support affect in the face of air pollution exposure. For instance, the air pollution threshold at which someone receives an air quality alert and the protective actions recommended to them could be personalized based on their ASAP. Through each of these pathways, ASAP provides opportunity to better integrate mental health in climate adaptation policies, plans, and programs.

Limitations and outlook

As ASAP-related studies develop, it is useful to consider several limitations of our analysis. First, our data featured relatively low overall levels of day-to-day variability in air pollution. Greater variance of air pollution might uncover stronger effects and larger differences in individuals’ ASAP. Second, air quality data for the five major pollutants was not consistently collected by the EPA during the iSAHIB study period. Our examination of missingness patterns provided no indication that the EPA’s air quality monitors worked selectively for particular pollutants on particular days. However, missingness in the pollutant data means that AQI values may be underestimated. Third, we used spatially monadic, county-level AQI data, which assumes that sensors at a single location characterize environmental exposures over a larger area. Since many people are mobile during the day and air pollution exposures vary over space and time [68, 69], air pollution data that is more specific to participants’ homes or mobility paths would enable more precise measurement of ASAP. Fourth, although age heterogeneous, the sample was racially, ethnically, and geographically homogeneous. In addition, iSAHIB data was collected in 2010 and 2011. Our results thus may not reflect the ASAP of more diverse populations today. Fifth, and importantly for interpretation of habituation findings, our analysis did not address the possibility that individuals may already be taking actions to protect themselves from air pollution, such as reducing outdoor exercise on more polluted days, and thus may underestimate individuals’ actual ASAP.

Future research on ASAP should consider incorporating more study sites to widen the range of AQI values, obtaining more granular measures of air pollution, sampling more diverse individuals, and collecting data about any protective actions that individuals are already taking to cope with air pollution. Moreover, future work can apply the methodology proposed here to expand the measurement of sensitivity to climate change (affective or otherwise) to other climate hazards, such as flooding or drought.

Conclusion

In this paper, we proposed a new intraindividual variability construct, affective sensitivity to air pollution (ASAP), that describes the extent to which an individual’s affective states fluctuate in accordance with daily changes in air pollution. Our empirical illustration found that there were indeed substantial interindividual differences in ASAP, suggesting that repeated measures of individuals’ day-to-day affect provide a new way of measuring sensitivity to climate change. As climate change threatens human health and wellbeing worldwide, ASAP contributes to discourse around the conceptualization and measurement of individuals’ climate vulnerability. This new construct can be leveraged to better integrate affect and mental health in climate adaptation policies, plans, and programs.

Supporting information

S1 Table. Sensitivity analyses examining subsets of emotions.

https://doi.org/10.1371/journal.pone.0307430.s001

(DOCX)

S1 Dataset. Data analyzed in this study.

https://doi.org/10.1371/journal.pone.0307430.s002

(CSV)

Acknowledgments

We extend our sincere thanks to the participants of the Intraindividual Study of Affect, Health, and Interpersonal Behavior (iSAHIB), who contributed data to this study. We are also grateful to the reviewers for their constructive feedback, which enhanced the manuscript.

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Figures

Abstract

Introduction

Air pollution and wellbeing

Affective Sensitivity to Air Pollution (ASAP)

Materials and method

Participants & procedure

Measures

Daily air pollution.

Daily affect (arousal and valence).

Day.

Data configuration and analysis

Results

Affect arousal

Affect valence

Discussion

Applications for policy and practice

Limitations and outlook

Conclusion

Supporting information

S1 Table. Sensitivity analyses examining subsets of emotions.

S1 Dataset. Data analyzed in this study.

Acknowledgments

References