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Unnecessary vehicle idling negatively contributes to air quality, which harms human health. The latter harms are greater when vehicles concentrate in an area frequented by vulnerable populations, such as children and hospital patients. The present experiments evaluated the effects of social-norm messages presented in a hypothetical school pickup zone on online drivers’ intent to idle. In Experiment 1, when messages were described as presented on a dynamic feedback display, much like those used to reduce speeding, they significantly decreased intent to idle. This effect was larger when a picture of a child accompanied the message. In Experiment 2, the social norm message plus picture significantly decreased intent to idle when four or fewer other drivers in the area were described as idling (i.e., ignoring the injunctive social-norm message). Future planned research will evaluate the efficacy of this dynamic display in reducing real idling behavior in high-idling zones frequented by vulnerable populations. 

Low-cost air quality sensors (LCSs) are becoming more ubiquitous as individuals and communities seek to reduce their exposure to poor air quality. Compact, efficient, and aesthetically designed sensor housings that do not interfere with the target air quality measurements are a necessary component of a low-cost sensing system. The selection of appropriate housing material can be an important factor in air quality applications employing LCSs. Three-dimensional printing, specifically fused deposition modeling (FDM), is a standard for prototyping and small-scale custom plastics production because of its low cost and ability for rapid iteration. However, little information exists about whether FDM-printed thermoplastics affect measurements of trace atmospheric gasses. This study investigates how five different FDM-printed thermoplastics (ABS, PETG, PLA, PC, and PVDF) affect the concentration of five common atmospheric trace gasses (CO, CO2, NO, NO2, and VOCs). The laboratory results show that the thermoplastics, except for PVDF, exhibit VOC off-gassing. The results also indicate no to limited interaction between all of the thermoplastics and CO and CO2 and a small interaction between all of the thermoplastics and NO and NO2. 

Combustion vehicle emissions contribute to poor air quality and release greenhouse gases into the atmosphere, and vehicle pollution has been associated with numerous adverse health effects. Roadways with extensive waiting and/or passenger drop-off, such as schools and hospital drop-off zones, can result in a high incidence and density of idling vehicles. This can produce micro-climates of increased vehicle pollution. Thus, the detection of idling vehicles can be helpful in monitoring and responding to unnecessary idling and be integrated into real-time or off-line systems to address the resulting pollution. In this paper, we present a real-time, dynamic vehicle idling detection algorithm. The proposed idle detection algorithm and notification rely on an algorithm to detect these idling vehicles. The proposed method relies on a multisensor, audio-visual, machine-learning workflow to detect idling vehicles visually under three conditions: moving, static with the engine on, and static with the engine off. The visual vehicle motion detector is built in the first stage, and then a contrastive-learning-based latent space is trained for classifying static vehicle engine sound. We test our system in real-time at a hospital drop-off point in Salt Lake City. This in situ dataset was collected and annotated, and it includes vehicles of varying models and types. The experiments show that the method can detect engine switching on or off instantly and achieves 71.02 average precision (AP) for idle detection and 91.06 for engine off detection. 

Formaldehyde is a known human carcinogen and an important indoor and outdoor air pollutant. However, current strategies for formaldehyde measurement, such as chromatographic and optical techniques, are expensive and labor intensive. Low-cost gas sensors have been emerging to provide effective measurement of air pollutants. In this study, we evaluated eight low-cost electrochemical formaldehyde sensors (SFA30, Sensirion®, Staefa, Switzerland) in the laboratory with a broadband cavity-enhanced absorption spectroscopy as the reference instrument. As a group, the sensors exhibited good linearity of response (R2 > 0.95), low limit of detection (11.3 ± 2.07 ppb), good accuracy (3.96 ± 0.33 ppb and 6.2 ± 0.3% N), acceptable repeatability (3.46% averaged coefficient of variation), reasonably fast response (131–439 s) and moderate inter-sensor variability (0.551 intraclass correlation coefficient) over the formaldehyde concentration range of 0–76 ppb. We also systematically investigated the effects of temperature and relative humidity on sensor response, and the results showed that formaldehyde concentration was the most important contributor to sensor response, followed by temperature, and relative humidity. The results suggest the feasibility of using this low-cost electrochemical sensor to measure formaldehyde concentrations at relevant concentration ranges in indoor and outdoor environments. 

Abstract. As the changing climate expands the extent of arid andsemi-arid lands, the number of, severity of, and health effects associated with dust events are likely to increase. However, regulatory measurements capable of capturing dust (PM10, particulate matter smaller than10 µm in diameter) are sparse, sparser than measurements of PM2.5 (PM smaller than 2.5 µm in diameter). Although low-cost sensors couldsupplement regulatory monitors, as numerous studies have shown forPM2.5 concentrations, most of these sensors are not effective atmeasuring PM10 despite claims by sensor manufacturers. This studyfocuses on the Salt Lake Valley, adjacent to the Great Salt Lake, whichrecently reached historic lows exposing 1865 km2 of dry lake bed. Itevaluated the field performance of the Plantower PMS5003, a common low-costPM sensor, and the Alphasense OPC-N3, a promising candidate for low-costmeasurement of PM10, against a federal equivalent method (FEM, betaattenuation) and research measurements (GRIMM aerosol spectrometer model1.109) at three different locations. During a month-long field study thatincluded five dust events in the Salt Lake Valley with PM10 concentrations reaching 311 µg m−3, the OPC-N3 exhibited strong correlation with FEM PM10 measurements (R2 = 0.865, RMSE = 12.4 µg m−3) and GRIMM (R2 = 0.937, RMSE = 17.7 µg m−3). The PMS exhibited poor to moderate correlations(R2 < 0.49, RMSE = 33–45 µg m−3) withreference or research monitors and severely underestimated the PM10concentrations (slope < 0.099) for PM10. We also evaluated aPM-ratio-based correction method to improve the estimated PM10concentration from PMSs. After applying this method, PMS PM10concentrations correlated reasonably well with FEM measurements (R2 > 0.63) and GRIMM measurements (R2 > 0.76), andthe RMSE decreased to 15–25 µg m−3. Our results suggest that itmay be possible to obtain better resolved spatial estimates of PM10concentration using a combination of PMSs (often publicly availablein communities) and measurements of PM2.5 and PM10, such as thoseprovided by FEMs, research-grade instrumentation, or the OPC-N3. 

Short-term exposure to fine particulate matter (PM2.5) pollution is linked to numerous adverse health effects. Pollution episodes, such as wildfires, can lead to substantial increases in PM2.5 levels. However, sparse regulatory measurements provide an incomplete understanding of pollution gradients. Here, we demonstrate an infrastructure that integrates community-based measurements from a network of low-cost PM2.5 sensors with rigorous calibration and a Gaussian process model to understand neighborhood-scale PM2.5 concentrations during three pollution episodes (July 4, 2018, fireworks; July 5 and 6, 2018, wildfire; Jan 3−7, 2019, persistent cold air pool, PCAP). The firework/wildfire events included 118 sensors in 84 locations, while the PCAP event included 218 sensors in 138 locations. The model results accurately predict reference measurements during the fireworks (n: 16, hourly root-mean-square error, RMSE, 12.3−21.5 μg/m3, n(normalized)-RMSE: 9−24%), the wildfire (n: 46, RMSE: 2.6−4.0 μg/m3; nRMSE: 13.1−22.9%), and the PCAP (n: 96, RMSE: 4.9−5.7 μg/m3; nRMSE: 20.2−21.3%). They also revealed dramatic geospatial differences in PM2.5 concentrations that are not apparent when only considering government measurements or viewing the US Environmental Protection Agency’s AirNow’s visualizations. Complementing the PM2.5 estimates and visualizations are highly resolved uncertainty maps. Together, these results illustrate the potential for low-cost sensor networks that combined with a data-fusion algorithm and appropriate calibration and training can dynamically and with improved accuracy estimate PM2.5 concentrations during pollution episodes. These highly resolved uncertainty estimates can provide a much-needed strategy to communicate uncertainty to end users.