Environmental information disclosure programs seek to motivate firms to reduce their environmental impact. A variety of environmental impacts are reported in these programs and often this information is aggregated into a composite environmental index (CEI) for easier communication. The challenge is to create a meaningful index that allows environmental performance to be compared over time and space without ambiguity. In this paper, we argue that it is important to develop a cardinally meaningful and standardized CEI and use a nonparametric frontier approach to constructing such an index. This approach has the advantage to handle issues associated with data irregularity and the mixed measurability of underlying variables. We apply this approach to construct a CEI for evaluating the environmental performance of manufacturing facilities in different industrial sectors in Los Angeles based on data from the toxic release inventory. We show how the CEI can be used to improve facility-level environmental performance. A sensitivity analysis is conducted with respect to the uncertainty in data accuracy, which demonstrates the robustness of the nonparametric frontier approach in constructing meaningful environmental indices.

Air pollution is one of the largest environmental health risks globally but is often imperceptible to people. Air quality smartphone applications (commonly called apps) provide real-time localized air quality information and have the potential to help people learn about the health effects of air pollution and enable them to take action to protect their health. Hundreds of air quality apps are now available; however, there is scant information on how effective these mobile apps are at educating stakeholders about air pollution and promoting behavioral change to protect their health. In this paper, we test how intrinsic and extrinsic motivations can enhance users' engagement with air quality information through the app, and favor changes in protective behavior. We developed an air quality app, AirForU, with a built-in research study that was downloaded by 2,740 users. We found that engagement was higher for users with intrinsic motivations, such as those who are health conscious, either because they are suffering from heart disease or other conditions aggravated by air pollution, or because they exercise often and want to maintain a healthy lifestyle. Extrinsic motivations such as notifications were also effective. App users stated that they frequently shared air quality with others, learned about the Air Quality Index (AQI), and took measures to protect their health while using the app.

Air pollution has a significant impact on health but is often invisible to the naked eye. Real-time air quality information can help people take action to protect their health. However, little is known on how to most effectively frame air quality information to promote public health. We conducted a field experiment to study people’s engagement with real-time air quality information provided through a smartphone application (app). We tested 12 different messaging strategies on both intent to engage with air quality information (through a survey), and actual engagement with air quality information tracked through the app in response to the messaging strategies. Our results, based on 835 survey respondents and 2,740 app users, show that intent to engage and actual engagement differ. Overall, users’ demographics were the most important predictor of engagement with messages. This research demonstrates the significance of testing messaging strategies through field experiments rather than through surveys, and the importance of targeted messages.

Air pollution is one of the largest environmental health risks globally but is often imperceptible by people. Air quality smartphone applications (commonly called apps) provide real-time localized air quality information and have the potential to help people learn about the health effects of air pollution and take action to protect their health. Hundreds of air quality apps are now available, however, there is scant information on how effective these mobile apps are at educating stakeholders about air pollution and at promoting behavioral change to protect their health. In this paper, we test how intrinsic and extrinsic motivations can enhance users' engagement with air quality information and favor changes in protective behavior. We developed an air quality app, AirForU, with a built-in research study that was downloaded by 2,740 users. We found that user engagement, measured as checking the app, and talking to someone about air pollution, was strong in the first few weeks after downloading the app but faded significantly after 12 weeks. Engagement was higher for users with intrinsic motivations, such as those who are health conscious, either because they are suffering from heart disease or other conditions aggravated by air pollution, or because they exercise often and want to maintain a healthy lifestyle. Extrinsic motivations such as notifications were also effective. App users stated that they shared air quality frequently with others while using the app, learned information about the Air Quality Index (AQI), and took measures to protect their health.

Aim: In this study, we intend to compare the linear dimensional changes of interocclusal recording media by immersing them in disinfectant solutions at different time intervals. Materials and methods: Five interocclusal recording materials were used for this study and were grouped according to material types, namely wax, zinc oxide eugenol impression paste, polyether, polyvinyl siloxane, and bisacryl bite registration material. Each material was manipulated and injected into a stainless steel die. The materials were divided into 5 groups with 5 subgroups of 10 samples with a total of 250 samples. The samples were subjected to immersion in 2% glutaraldehyde and 0.5% sodium hypochlorite each for 30 and 60 minutes. Linear dimensional changes of the samples were tested by measuring the distance between points A and B at different time intervals by means of a stereomicroscope and compared with the control group. Results: Bisacryl showed the least linear dimensional change when immersed in both the solutions. Conclusion: Bisacryl (Luxabite) presented no linear dimensional change at both time intervals as opposed to the other materials, hence, it is most accurate.