Abstract
BACKGROUND:
Little is known about how individuals are exposed to air pollution in various daily activity spaces due to a lack of data collected in the full range of spatial contexts in which they spend their time. The limited understanding makes it difficult for people to act in informed ways to reduce their exposure both indoors and outdoors.
OBJECTIVE:
This study aimed to (1) assess whether personalized air quality data collected using GPS-enabled portable monitors (GeoAir2), coupled with travel-activity diaries, promote people’s awareness and behavioral changes regarding indoor and outdoor air pollution and (2) demonstrate the effect of places and activities on personal exposure by analyzing individual exposure profiles.
METHODS:
44 participants carried GeoAir2 to collect geo-referenced air pollution data and completed travel-activity diaries for three days. These data were then combined for spatial data analysis and visualization. Participants also completed pre- and post-session surveys about awareness and behaviors regarding air pollution. Paired-sample t-tests were performed to evaluate changes in knowledge, attitudes/perceptions, and behavioral intentions/practices, respectively. Lastly, follow-up interviews were conducted with a subset of participants.
RESULTS:
Most participants experienced PM2.5 peaks indoors, especially when cooking at home, and had the lowest exposure in transit. Participants reported becoming more aware of air quality in their surroundings and more concerned about its health effects (t = 3.92, p = 0.000) and took more action or were more motivated to alter their behaviors to mitigate their exposure (t = 3.40, p = 0.000) after the intervention than before. However, there was no significant improvement in knowledge (t = 0.897; p = 0.187).
SIGNIFICANCE:
Personal exposure monitoring, combined with travel-activity diaries, leads to positive changes in attitudes, perceptions, and behaviors related to air pollution. This study highlights the importance of citizen engagement in air monitoring for effective risk communication and air pollution management.
Keywords: Air pollution, environmental monitoring, geospatial analyses, particulate matter, personal exposure, sensors
INTRODUCTION
Exposure to air pollution is associated with a range of short-term and chronic health impacts, such as asthma, heart attack, lung cancer, and premature death [1, 2]. The main cause of air pollution is human activities, including burning fossil fuels for motor vehicles, power generation, and industrial processes, all of which release significant amounts of gases and particles into the air that people breathe outdoors [3]. Air pollution can also be generated from smaller, everyday activities that people perform indoors, such as cooking, cleaning, smoking, or burning candles, increasing the overall burden of disease attributable to air pollution [4]. However, while the problem of outdoor air pollution has been widely studied and regulated, indoor air quality has gained much less attention in the literature and is largely unregulated [5]. Little is known about how people are exposed to air pollution during their daily activities in various indoor and outdoor microenvironments (e.g., homes, offices, schools, shops, vehicle interiors, and public spaces). Due to this significant knowledge gap, preventive measures to reduce exposure to indoor air pollution have been limited.
The discrepancy in the attention given to controlling outdoor and indoor air pollution can be partially explained by the difference in the levels of public awareness about outdoor and indoor air pollution and associated risk perceptions. Because most exposure or health risk assessments have been based exclusively on data drawn from ambient air monitoring networks, indoor air quality has been largely unmonitored and understudied [6]. A lack of comprehensive data and the invisibility of air pollution make it difficult for people to act in informed ways to reduce their exposure both indoors and outdoors [7]. However, a growing body of scientific evidence shows that indoor air can be two to five times more contaminated than the outdoor air, even in large cities that are highly industrialized [8]. As most people spend approximately 90% of their time indoors [9], the health risks associated with indoor air pollution may be higher than those associated with exposure to outdoor pollutants. Therefore, it is important to monitor both outdoor and indoor air quality at a fine granularity to better understand spatiotemporal variations in exposure, improve citizen awareness, and promote behavioral changes to reduce overall health risks related to air pollution.
As one strategy to promote public awareness and behavioral changes, recent studies have increasingly used low-cost air sensor technologies to engage citizens in local air quality monitoring [10–12]. Many of these studies have implemented participatory air sensing interventions in which volunteers are involved in community air monitoring by installing low-cost air sensors at multiple outdoor locations in their community, including near their homes. Although most of the literature focuses on public involvement in outdoor air quality monitoring and management, a few studies have investigated home exposure by placing air sensors inside participants’ homes [13, 14]. However, such a stationary monitoring approach does not account for human mobility and excludes people’s exposure to air pollution in other indoor environments [15]. Furthermore, it often fails to satisfy people’s curiosity because it does not offer a comprehensive picture of personal exposure in the full range of spatial contexts in which they actually spend their time [7].
A growing number of studies have found that people tend to be more motivated to alter their behaviors when they are offered personalized information about their risk and informed of the air quality in their immediate surroundings [16, 17]. In this sense, using a portable air monitor may be a promising strategy because it enables citizens to collect air quality data in any place in which they spend their time, including indoor spaces [17–19]. When these highly localized, personalized air pollution data are combined with other data sets, such as individuals’ travel pattern data (e.g., global positioning system [GPS] data) and activity diaries, they can significantly enhance an understanding of the effect of places and activities on personal exposure to air pollution [20, 21]. This information would allow people to make connections between their activities, activity locations and times, and resulting changes in air quality [14], helping them better identify sources of air pollution in their surroundings and possible ways to reduce their exposure. However, few empirical studies have evaluated how citizens respond to mobile air sensor technologies and whether the participatory air monitoring strategy using portable sensors, coupled with travel-activity pattern data, leads to changes in individuals’ awareness, attitudes, and practices regarding indoor and outdoor air pollution. While some studies have examined the effect of mobile sensors on perceptions and behaviors, they have focused mainly on outdoor air monitoring, requesting participants to carry the sensor only during commutes or to mount it on their vehicles [7, 10], or did not collect travel-activity pattern data [11].
To fill these gaps in the literature, this study aims to provide empirical evidence that geo-referenced, real-time air quality data collected through participatory air sensing using portable monitors, in combination with travel-activity pattern data, affects people’s knowledge, attitudes, and behaviors related to indoor and outdoor air pollution. It also presents individual exposure profiles and their summary statistics to demonstrate how people’s travel and activity patterns can explain the spatiotemporal variations in their exposure in various microenvironments. This research uses GeoAir2, a low-cost portable air monitor, which can collect both PM2.5 (particulate matter less than 2.5 μm in diameter) and location data due to its built-in GPS.
MATERIALS AND METHODS
Study materials
Portable air monitor.
This study utilizes a GeoAir2 portable air-monitoring system, which integrates a PM2.5 sensor (SPS30, Sensirion, Stäfa, Switzerland), GPS module, temperature/humidity sensor, data logger, LCD screen, and a battery that lasts up to 15 hours. It is portable/wearable using a belt clip, carabiner, or shoulder strap (Fig. 1). GeoAir2 is suitable for citizen-engaged research and geospatial assessments of personal exposure due to its ease of use and ability to track user locations [19]. The monitor records PM2.5 measurements (μg/m3) every minute, as well as GPS locations, a list of nearby Wi-Fi media access control (MAC) addresses for indoor geolocation, temperature, humidity, date, time, and more. Data are stored locally as an encrypted file for data confidentiality. Unlike many commercially available devices, GeoAir2 does not require a connection to a local Wi-Fi network or a constant Bluetooth connection to a user’s smartphone to record and view data due to an internal data logging capacity and display screen. It also minimizes the users’ burden by only requiring them to charge the device daily. Considering that not everyone has a strong internet connection and is technically capable, GeoAir2 is an ideal instrument for citizen-engaged research because it ensures participant diversity and inclusivity [19]. Several laboratory and field evaluations have demonstrated its high accuracy and reliability when compared to reference instruments [19, 22, 23]. The detailed specifications of GeoAir2 and a full list of data recorded by it are described in Park et al. [19].
Fig. 1. Portable air monitor and the three ways to carry/wear the monitor.
(First) GeoAir2 PM2.5 monitor. (Second) A way to wear GeoAir2 using a shoulder strap. (Third) A way to wear GeoAir2 with a belt clip. (Fourth) A way to carry GeoAir2 using a carabiner.
Travel-activity diaries.
Because human activities can determine not only the levels of air pollution and exposure but also the location and timing of exposure, it is crucial to collect travel-activity pattern data as part of exposure research [9]. In this study, a travel-activity diary was developed in both English and Spanish. The diary includes questions about the type of place visited (i.e., home, workplace/school, and transit stop), type of activity performed, exact time they started the activity, and travel mode used (Fig. 2). The Research Electronic Data Capture (REDCap), a secure web program for developing and managing online surveys and databases [24], was used to create the travel-activity diary, consent forms, and all other surveys. The benefit of using a web-based travel-activity diary over a paper-based one is that people can access and complete the diary in real-time, using their smartphone [25]. This can significantly reduce recall bias and the probability of a mismatch between the actual and reported time and location at which an activity was performed.
Fig. 2. An example of a travel-activity diary data entry.
(Left) A mobile version of a travel-activity diary (Right) (cont.).
Demographic survey.
A survey was administered to obtain basic demographic information, including gender, age, educational attainment, household income level, employment status, occupation type, and smoking status. Participants also provided the physical addresses of their homes and workplaces/schools. All questions offered the option “prefer not to respond”.
Awareness/behavior survey and scoring.
Participants’ perceptions, attitudes, and practices regarding indoor and outdoor air quality were assessed through 20 survey questions, which cover the following thematic areas: (1) knowledge; (2) attitudes and perceptions; and (3) behavioral intentions and practices.
Knowledge:
Knowledge of air quality was evaluated by asking about possible sources of outdoor and indoor air pollution, as well as health implications. The number of selected options was summed for each question, where higher counts indicated a better understanding of air pollution. Participants could choose “don’t know”, which was coded as 0 for the analysis.
Attitudes and perceptions:
Participants were asked to rate the overall quality of the air they breathe in their community, inside their home, in other indoor spaces in which they spend their time, and while traveling. These questions were rated from “very poor” to “very good” with a “don’t know” option. For the analysis, all responses were recorded as 1 except for “don’t know”, which was recorded as 0. They were also asked how dangerous they think air pollution is to their health (on a Likert scale from 1 = not at all dangerous to 5 = very dangerous; and 0 = don’t know), how worried they are about the potential effects of outdoor/indoor air pollution on their health (from 1 = not at all worried to 5 = very worried; and 0 = don’t know), to what extent they consider outdoor/indoor air quality management to be under their control (from 1 = totally out of my control to 5 = totally under my control; and 0 = not sure), and how confident they are in reducing their exposure indoors and outdoors (from 1 = not at all confident to 5 = very confident; and 0 = don’t know).
Behavioral intentions and practices:
Behavioral intentions and practices were assessed through questions about participants’ intentions to act and their actions actually taken during the past month to improve their outdoor/indoor air quality or reduce their exposure. In addition to the list of behaviors, the questions about intentions included the option “I have never considered taking action”, which was recorded as 0 for the analysis. The question about action actually taken had two other options: “I have not taken any action for the past month, but I could do so in the future” and “I have not taken any action for the past month, and I have no intention of doing so in the future”. The passive response (“I have not … and I have no intention”) was recorded as 0, whereas the affirmative responses had a value greater than or equal to 1 (i.e., 1 = “I have not…but I could do so in the future”; 1+ the number of behaviors that they intend to change or have changed). The higher number indicates the participants’ greater intention or effort to reduce indoor/outdoor air pollution and their exposure. Participants were also asked how often they had paid attention to air quality or talked to others about local air quality or potential health risks during the past month (from 1 = never to 5 = very frequently).
This survey was conducted before and after air monitoring using GeoAir devices. Participants completed a pre-session survey during training and a post-session survey four weeks after the data collection. More details are described in the next subsection.
Recruitment and data collection
This study focused on the Latino/Hispanic population in eastern North Carolina (ENC). Latino/Hispanic participants aged 18–64 were recruited in fall 2021, and 44 completed all the required tasks among 45 enrolled. Many Latinos in this region are recent immigrants who moved for employment. Although many work in highly polluting industries, such as construction, restaurants, manufacturing, and agriculture, they are often underrepresented in environmental health research or policy decision-making processes. A partnership with a non-profit organization, the Association of Mexicans in North Carolina (AMEXCAN), was formed to engage with the Latino/Hispanic population in ENC. The project materials were co-developed to ensure that they were culturally and linguistically appropriate. The AMEXCAN also assisted the university research team with recruitment by leveraging their network and social media platforms, distributing the study flyer to Hispanic-serving local businesses, and using a door-knocking method. Multiple in-person and virtual training sessions were co-organized, during which participants completed a demographic survey and a pre-session survey about awareness and behaviors related to air pollution. Participants were also instructed on how to complete travel-activity diaries and use the air monitor.
Participants were asked to carry the air monitor and complete travel-activity diaries online during the specified study period, which comprised two weekdays and one weekend day (Fig. 3). The three-day period was chosen because the risk of participants dropping out due to being overwhelmed by continuous use can outweigh the benefits of additional insights from longer-term data collection [7]. Once the air monitor was returned, the data were analyzed, and a printed copy of the data analysis results was mailed to each participant. After they received the results, an individual meeting with each participant was held by video or phone call to engage citizens in the data interpretation. They offered additional details about their data by recalling specific events that occurred during the data collection. This recollection was possible because participants were encouraged to check the real-time air quality frequently, especially when performing a new activity or visiting a new place. Four weeks after the meeting, participants completed a post-session survey with the same set of questions as those in the pre-session survey. The four-week design was intended to provide participants with enough time to develop new behaviors [11]. Finally, follow-up interviews were conducted with selected participants to further understand how they felt about the portable air monitor, their data, and the data visualization products, as well as whether the method used in this study promoted their awareness and behavioral changes.
Fig. 3. Data collection workflow.
The community partner AMEXCAN provided consistent input on the implementation of the project. Among 51 people who showed interest, 45 people signed the consent form and were enrolled in the study, and 44 completed all the required tasks.
Data analysis
Spatial data analysis and visualization.
For data integration, this study utilized a geospatial method developed by Park [26], which uses GPS and Wi-Fi MAC address data collected via GeoAir2 as well as participants’ geocoded home, workplace, and school addresses to (1) restore the geographic coordinates of visited locations captured inaccurately by GPS (especially indoor locations) and (2) classify each location into different types of microenvironments (i.e., homes, workplaces/schools, other places, and in-transit). The outcome of this method was then combined with travel-activity diaries by matching times and locations. Automated and manual inspections were conducted for all participants’ travel-activity diaries to ensure the data quality and to minimize human errors that may have occurred during data entry. All data cleaning and integration processes were completed using R 4.0.3 and Python 3.8.9.
Using the final output, summary statistics for PM2.5 concentrations in each microenvironment were calculated. Also, two time-series graphs were created for each individual: one visually links the types of microenvironments participants stayed, the activities they conducted in each place, and the corresponding PM2.5 concentrations, and the other shows the PM2.5 concentrations and corresponding health concerns defined by the United States Air Quality Index [27]. For visualization purposes, any PM2.5 concentrations exceeding 500 μg/m3 were replaced with 500 μg/m3 to improve the readability of the graphs. A map was also created to highlight the spatial variations in concentrations along the participant’s movement path. The AirSensor (1.0.8) and Leaflet (2.0.4.1) R packages were used for data visualization.
Statistical analysis.
To evaluate participants’ changes in awareness/behaviors related to air pollution before and after the intervention, paired-sample t-tests were performed to compare the mean scores between the pre- and post-sessions for each thematic area. All the tests were one-tailed, and an alpha value of 0.05 or less was considered statistically significant. The statistical analyses were conducted using Python 3.8.9.
RESULTS
Demographic characteristics of participants
Table 1 shows the demographic characteristics of the 44 study participants. The sample was predominantly female, with an average age of 38.63 years. Sixty-four percent had a high school degree or some college experience, and 61% were from low-income households (less than $40,000). The majority were employed (66%), with their occupations including jobs related to food preparation/serving, construction/extraction, education/training/library, and management. Two participants reported themselves as smokers. Participants resided in rural or suburban counties.
Table 1.
Demographic characteristics of participants.
| Variables | Total N (%) 44 (100) |
|---|---|
| Gender | |
| Male | 10 (22.73) |
| Female | 34 (77.27) |
| Age | |
| 18–24 | 8 (18.18) |
| 25–39 | 15 (34.09) |
| 40–59 | 21 (47.73) |
| 60 plus | 0 (0.00) |
| Educational Attainment | |
| Less than high school diploma | 6 (14.00) |
| High school diploma | 14 (32.00) |
| Some college or associate degree | 14 (32.00) |
| Bachelor’s degree or higher | 9 (20.00) |
| PNRa | 1 (2.00) |
| Employment Status | |
| Employedb | 29 (65.91) |
| Unemployedc | 14 (31.82) |
| PNR | 1 (2.27) |
| Occupation Types | |
| Food preparation and serving related occupations | 5 (17.24) |
| Construction and extraction occupations | 4 (13.79) |
| Management occupations | 4 (13.79) |
| Education, training, and library occupations | 4 (13.79) |
| Office and administrative support occupations | 2 (6.90) |
| Community and social services occupations | 2 (6.90) |
| Architecture and engineering occupations | 1 (3.45) |
| Healthcare support occupations | 1 (3.45) |
| Sales and related occupations | 1 (3.45) |
| Personal care and service occupations | 1 (3.45) |
| Building and groups cleaning and maintenance | 1 (3.45) |
| Installation, maintenance, and repair occupations | 1 (3.45) |
| PNR | 2 (6.90) |
| Household Income | |
| ≤$39,999 | 27 (61.36) |
| $40,000–$74,999 | 3 (6.82) |
| ≥$75,000 | 2 (4.55) |
| PNR | 12 (27.27) |
| County of Residence | |
| Regional city and suburban countyd | 28 (63.64) |
| Rural countye | 16 (36.36) |
| Smoking Status | |
| Smoker | 2 (4.00) |
| Non-smoker | 42 (96.00) |
PNR Prefer not to respond
Employed: full-time, part-time, self-employed, and students with a job
Unemployed: unemployed, retired, stay-at-home parents or spouses, and students without a job
Rural county: Beaufort, Craven, Duplin, Greene, Lenoir, and Washington Counties, NC
Regional city and suburban county: Pitt and Onslow Counties, NC.
The effect of places and activities on personal exposure
On average, people spent 75% of their time at home, 9% at workplaces/schools, 5% in transportation, and 11% in other places (Table 2). The analysis result shows that the locations categorized as “other places” had the highest mean and SD values in PM2.5 concentrations (35.37 ± 181.14 μg/m3). The activities performed in the “other places” include performing civic/religious activities, eating out, using drive-through services, shopping, and using a private car service. For example, one participant encountered a “hazardous” level of PM2.5 while attending a bonfire with outdoor grilling (1217.94 μg/m3). Many Mexicans frequently participate in bonfires because this is a way they socialize and an important part of Mexican culture. This result indicates that various activities and sources in religious, recreational, and social venues or other public buildings produce PM2.5 over a wide range of values. Home locations had the second highest mean and SD (14.98 ± 103.92 μg/m3), which reflects various pollution-generating activities occurring at home as well as different levels of availability of preventive measures (e.g., a lack of a ventilation system) between households.
Table 2.
Summary statistics for PM2.5 concentrations in each microenvironment.
| Microenvironment | n | Average Fraction of a Day Spent | PM2.5 (μg/m3) | |
|---|---|---|---|---|
| Mean | SD | |||
| Home | 132,382 | 75% | 14.98 | 103.92 |
| Work/School | 16,534 | 9% | 7.89 | 33.84 |
| In-transit | 8,347 | 5% | 3.66 | 9.77 |
| Other place | 18,489 | 11% | 35.37 | 181.14 |
In contrast, almost all participants had low levels of exposure during traveling (3.66 ± 9.77 μg/m3), indicating its minimal contribution to individuals’ total daily exposure due to the lowest mean concentration and smallest time fraction of the day. It may be because PM2.5 concentrations in rural ENC communities are not as poor as in big cities on typical days or many of them might have driven cars with windows closed. However, there were a few exceptions. A few participants were exposed to the PM2.5 concentrations ranging from 46.93 to 356.51 μg/m3 while idling at a traffic light with windows open. Another participant experienced “unhealthy” levels of PM2.5 when they were walking around their neighborhood when their neighbor was burning waste (116.16 μg/m3).
Figure 4 includes a data visualization example of two time-series graphs that show how PM2.5 concentrations, location data, and travel-activity diaries are combined using geographic information systems to analyze an individual exposure profile. The visualization results also highlight that people experienced highly variable PM2.5 concentrations throughout the day depending on the microenvironment visited and activity performed in the place. In addition, the individuals’ daily exposure profiles were widely different even if they lived in the same home (e.g., married couples) or in the same or nearby neighborhood.
Fig. 4. Two time-series graphs for one participant’s data.
(Top) This graph visually links PM2.5 concentrations, place types, and activities. (Bottom) This graph shows the PM2.5 concentrations and corresponding health concerns.
Most participants experienced PM2.5 peaks indoors when they performed activities that generated particle pollutants without adequate ventilation. In many cases, the timings of many high peaks in participants’ data corresponded to when they were cooking at home. Several participants reported that many traditional Mexican cuisines include frying and grilling and they observed a significant amount of smoke and high readings on the air monitor while grilling steaks, frying tacos with oil, cooking Mexican chorizo, using an oven, and burning food. Such oil-based cooking typically emits very high levels of fine particles [28].
Some participants were exposed to the “hazardous” levels of PM2.5 when they were cleaning (vacuuming) (633.13 μg/m3), gathering around an indoor fireplace (505.52 μg/m3), using a hair dryer (2032.92 μg/m3), and burning incense sticks or smoking at home (2209.64 μg/m3). For example, as shown in Fig. 4, one participant experienced multiple sharp spikes in PM2.5 at his home and office. Although activity markers were not placed at the peaks on the graph due to incomplete travel-activity diary data, the participant informed us during the individual meeting that very high PM2.5 levels were observed on the monitor while smoking cigarettes and burning incense sticks at home. (Note: a personalized exposure map—another visualization output shared with participants—is not presented to protect participants’ geoprivacy.)
Some participants experienced the highest pollution peaks which reached up to the “hazardous” level at their workplaces or worship places. For a participant working on a construction site, two peaks of “unhealthy” and “hazardous” PM2.5 levels were observed during cutting bricks at work (Fig. 5A). Other examples of exposure to “moderate” to “hazardous” levels of PM2.5 at workplaces include a person with a job in furniture refurbishing while removing old paint from furniture, a participant working in a kitchen with three grills, and a person working at a dry cleaner. Lastly, 4 participants were exposed to “unhealthy” to “hazardous” levels of PM2.5 during religious activities at churches possibly due to incense or candles (Fig. 5B). It means that people who spend extended periods inside poorly ventilated churches, such as church workers or worshippers who frequent churches could be endangering their health [29, 30].
Fig. 5. Examples of PM2.5 peaks at workplaces and places of worship.
A This participant works at the construction site. A pollution peak was observed when he was cutting bricks at work. B This participant was exposed to the hazardous level of PM2.5 during religious activities due to burning incense and candles inside the church.
Changes in knowledge, attitudes and perceptions, and behavioral intentions and practices
A series of paired sample t-tests was performed to test if there were any differences in scores between pre- and post-session surveys (Fig. 6). There was no significant difference in the knowledge of potential sources of air pollution and possible health effects between pre- and post-sessions (t = 0.897; p = 0.187; Table 3). This indicates that using a portable sensor may not directly lead to ameliorated knowledge of the health outcomes associated with air pollution if people did not experience specific symptoms during exposure or did not encounter some of the sources listed in the survey. Also, some sources that participants identified, such as hair dryers and a barge/boat with a flat deck used for transportation were not listed in the survey. Future studies may include an “other (please specify)” option to allow participants to write in a source that is recognized but not provided in the answer list. Although the change in knowledge scores was not discernible, the follow-up interviews with some respondents suggested that they reported being more knowledgeable about sources of air pollution after using the portable monitor. For example, one participant said as follows:
I didn’t know that when I cooked inside my house, the air we breathed would become a little toxic if I didn’t ventilate the house. That was something new I had learned... This community is between rural and non-rural... We don’t live in a city full of smog and everything, but even then, with this little device that I used, I became aware that smog is not the only contributor to toxic air, but there are other contributors. [Participant D]
Fig. 6. Box plots of scores for the three thematic areas in the awareness/behavior survey.
Three thematic areas include attitudes and perceptions, knowledge, as well as behavioral intentions and practices.
Table 3.
Paired sample t-tests of knowledge, attitudes, and perceptions, as well as behavioral intentions and practices.
| Highest Possible Score | Pre-Session Mean (±SD) | Post-Session Mean (±SD) | t-Test | p-Value | |
|---|---|---|---|---|---|
| Knowledge | 30 | 11.00 (5.26) | 11.61 (4.51) | 0.897 | 0.187 |
| Attitudes and perceptions | 39 | 23.45 (4.97) | 26.27 (5.16) | 3.922 | 0.000 |
| Behavioral intentions and practices | 59 | 21.20 (11.36) | 27.68 (10.58) | 3.415 | 0.000 |
There was a significant increase in attitude and perception scores (t = 3.92, p = 0.000). After using the portable air sensor, individuals reported becoming more aware of air quality in their surroundings and worrying more about its effects on their health, as the following comments from our interviewees indicate:
I was surprised by the exposure at my workplace. At my workplace, it [PM2.5 concentrations] came out very high. I didn’t expect that it would be that high. [Participant C]
I don’t think about the air quality that’s around me that often. I really don’t. I didn’t think before this study that it [air quality] played such a big role in my overall health. [Participant A]
Some subjects also recognized the importance of recording daily activities and travels as well as using the portable sensor to better understand their daily exposure:
I genuinely thought that the study was interesting, mostly because it gave me the opportunity to log in to my daily activities, which gave me a better perspective as to how I go about my regular days and where and how often I visit locations. [Participant A]
...if I didn’t have to input so much information [for travel-activity diaries], but then if I don’t, how am I going to know what causes the exposure? [Participant E]
Notably, there was a larger increase in the health risk perception of indoor air pollution (before: mean 3.00, SD 1.12; after: mean 3.27, SD 1.13) than that of outdoor air pollution (before: mean 3.09, SD 1.16; after: mean 3.13, SD 0.95). It may be because most participants experienced most pollution peaks indoors due not only to various indoor sources but also to a large amount of time spent indoors, as Ott [31] pointed out, “. . . we are basically an indoor species”. “In a modern society, total time outdoors is the most insignificant part of the day, often so small that it barely shows up in the total”. This finding suggests that researchers should be cautious when using ambient concentrations as a proxy for personal exposure.
Participants also felt more confident in their ability to control air quality both indoors and outdoors and to reduce their exposure than before the study. However, both before and after the study, they reported higher levels of confidence in those abilities regarding indoor air quality than outdoor air quality. These findings demonstrate that engaging citizens in air monitoring is an essential component of risk communication policies and a powerful way to improve the awareness of and attitudes toward air pollution [32].
Finally, a paired samples t-test found a significant increase in behavioral intention and practice scores (t = 3.40, p = 0.000). Table 4 presents the changes in the total number of responses for each behavior to mitigate indoor and outdoor air pollution and exposure between pre- and post-sessions. The development of new behaviors was also identified during follow-up interviews:
...before the study, I would typically light incense stick in my apartment. During the study, I actually cut out that act because I realized how much it polluted the air that I was breathing in. It is crazy to think how close I would sit to a burning incense stick...So I definitely learned a lot during and after the study...there are a lot of things I have changed since then, a lot of habits that I have cut out to be a little bit more conscious of my health. [Participant A]
Table 4.
Changes in behavioral intention and practice to reduce air pollution and exposure.
| Pre-session: Intended action | Post-session: Intended action | Pre-session: Action taken | Post-session: Action taken | |
|---|---|---|---|---|
| Behaviors to Improve Indoor Air Quality | ||||
| Opening windows to air out your home, car, workplace, or other indoor spaces. | 30 | 38 | 26 | 33 |
| Closing windows to protect yourself from outdoor air pollution. | 17 | 20 | 15 | 15 |
| Ventilating your cooking area to the outdoors using an exhaust fan or ducted range hood when cooking. | 36 | 42 | 27 | 33 |
| Changing air filters or using medium- or high- grade air filters. | 37 | 40 | 28 | 35 |
| Using an air purifier with a medium- or high- grade filter. | 13 | 18 | 15 | 12 |
| Reducing your use of cleaning products or using environmentally safe cleaning products. | 13 | 17 | 7 | 18 |
| Reducing your use of gas/wood-burning stoves, fireplaces, or candles or using an electric stove. | 19 | 19 | 14 | 22 |
| Removing mold or using a dehumidifier and/or air conditioner to reduce humidity. | 20 | 25 | 16 | 16 |
| Avoiding smoking/vaping indoors or encouraging others to avoid smoking/vaping indoors. | 17 | 23 | 11 | 12 |
| Educating your family, friends, or neighbors about the harmful effect of indoor air pollution on health and effective ways to improve indoor air quality. | 17 | 20 | 13 | 16 |
| I have never considered taking action to improve my indoor air quality. | 0 | 2 | – | – |
| I have not taken any action for the past month, but I could do so in the future. | – | – | 7 | 1 |
| I have not taken any action for the past month, and I have no intention of doing so in the future. | – | – | 0 | 0 |
| Behaviors to Improve Outdoor Air Quality | ||||
| Reducing the number of trips you take in your car by combining errands. | 17 | 19 | 15 | 24 |
| Choosing cleaner travel modes (e.g., walking, biking, sharing a ride, or using public transportations). | 11 | 18 | 11 | 25 |
| Reducing energy consumption at home, at work, and everywhere. | 21 | 25 | 18 | 20 |
| Trying alternatives to burning household waste/leaves (e.g., composting, recycling, using landfill/trash collection services, or using a formal dumpsite). | 14 | 23 | 11 | 19 |
| Avoiding/reducing your use of gas-powered equipment for landscaping or gardening. | 8 | 9 | 7 | 11 |
| Avoiding excessive idling of your automobiles. | 18 | 21 | 9 | 14 |
| Keeping your car, boat and other engines properly tuned or avoiding engines that smoke. | 18 | 28 | 13 | 18 |
| Changing travel and exercise routes to avoid high-traffic areas or traveling at less polluted times. | 14 | 17 | 11 | 15 |
| Avoiding outdoor activities or reducing the amount of time spent at vigorous activity during rush hours or when the forecast says unhealthy air quality. | 7 | 10 | 5 | 15 |
| Educating your family, friends, or neighbors about the harmful effect of air pollution on health and effective ways to improve the air quality in the community. | 15 | 19 | 12 | 13 |
| Checking daily air pollution forecasts in your community. | 9 | 16 | 5 | 9 |
| Closing windows to protect yourself from outdoor air pollution. | 15 | 21 | 12 | 17 |
| Taking protective measures, such as wearing a mask when your exposure level is expected to be high. | 18 | 23 | 16 | 23 |
| I have never considered taking action to improve the air quality in my community or to reduce my exposure to outdoor air pollution. | 7 | 1 | – | – |
| I have not taken any action for the past month, but I could do so in the future. | – | – | 11 | 3 |
| I have not taken any action for the past month, and I have no intention of doing so in the future. | – | – | 2 | 2 |
For participants, collecting real-time air quality data from their surrounding environments using a portable air monitor is an interactive learning experience [14, 18]. This study found that such learning experience and the geo-referenced air quality data coupled with travel-activity diary data can foster positive behavioral changes because it allows people to link their activities, activity locations and times, and subsequent changes in PM2.5 concentrations.
DISCUSSION
The findings of this study suggest that participatory sensing using mobile air sensors can have a positive impact on people’s attitudes, perceptions, and behaviors regarding indoor and outdoor air quality. This conclusion is consistent with previous research that reported positive associations between personalized data provided by low-cost sensors and awareness of air pollution and protective behaviors [13, 14]. However, it is contrasting with other studies that did not find significant changes in behaviors [10, 11, 32]. This discrepancy may be possibly due to the differences in the characteristics of study areas/populations, smaller sample sizes (n = 12, 22, and 36), different types of measured pollutants (e.g., NO2 and VOCs), and the study designs/methods. For instance, Boso et al. [32] focused on the most vulnerable population—low-income and elderly people who faced many obstacles related to self-protective behaviors and lived in a highly polluted city in southern Chile where air quality management is more challenging at an individual level and requires long-term, collective effort. Future research is needed to examine whether the methods used in this study can generate consistent results in other contexts and population groups.
The spatial data analysis results provide an insight into the dynamic nature of personal exposure to PM2.5 in various indoor/outdoor spaces, with a special emphasis on indoor spaces as an important spatial context for a holistic approach to personal exposure assessments. Consistent with previous studies [4, 29, 33], our finding highlights the importance of improved ventilation not only in homes but also at workplaces or in other indoor spaces, including places of worship to prevent repeated exposure to harmful pollutants in everyday life. Because it reveals that even people who do not live in a large city can be exposed to dangerous levels of PM2.5 indoors, it is important to educate both urban and rural residents about various pollution sources in their daily activity space as well as the associated risk management strategies. A thoroughly considered action plan should also be developed to manage indoor air quality at both national and local levels.
There are some limitations of this study. First, this study identified a short-term change, but further research is needed to understand long-term effects. Second, there were some mismatches between the self-reported information in travel-activity diaries and the information automatically detected by GPS data. This happened when not all activities/travels were reported or there was a recall bias when the diary was not completed in real-time. However, the impact of such data mismatch on the findings is minimal because most data mismatches were addressed by the automatic place detection method, and more details that participants provided about their data during the meeting helped understand the data better even with some missing information. Lastly, because this study focuses on rural/suburban residents in ENC, the findings might not be generalized to people in large cities or newly industrialized countries where outdoor air pollution is a serious concern.
Despite the limitations, this study represents a significant step in understanding the effect of places and activities on personal exposure and the importance of personalized risk information in changing people’s perceptions and behaviors. Integrating geo-referenced air quality measurements with travel-activity pattern data plays a key role in understanding spatiotemporal variations in exposure and empowering citizens to evaluate the air quality of the microenvironments they frequent and modify their daily behaviors by enabling them to link air quality to where they go and what they do. This study shows that GeoAir2 holds significant potential for citizen-engaged research aimed at assessing personal exposure and that data generated from it can form the basis of various mitigation and intervention strategies.
Supplementary Material
FUNDING
Research reported in this publication was supported by the National Institute of Environmental Health Sciences of the National Institutes of Health (NIH) under Award Number P30ES025128. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Footnotes
COMPETING INTERESTS
The authors declare no competing interests.
ETHICAL APPROVAL
The project protocol and materials were approved by the University and Medical Center Institutional Review Board at East Carolina University on June 16, 2020 (IRB#: UMCIRB 20–001302).
ADDITIONAL INFORMATION
Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41370-022-00515-9.
Reprints and permission information is available at http://www.nature.com/reprints
DATA AVAILABILITY
The data that support the findings of this study are not publicly available because they include personally identifiable information that could compromise research participant privacy/consent.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are not publicly available because they include personally identifiable information that could compromise research participant privacy/consent.