The purpose of this study was to assess health-related responses to wildfire smoke on social media. We examined whether seasonal wildfire smoke is an active topic on Twitter, the correlation between fine particulate matter (PM2.5 ) and Twitter search terms, and dimensions of community-level expression to wildfire smoke through tweets. Search terms were identified using a conceptual model developed and refined by healthcare providers and public health experts. Wildfire-related tweets were downloaded from Twitter users in Spokane, Washington during the 2017 and 2018 wildfire seasons. PM2.5 data were correlated with the search terms. A subset of tweets was deductively and then inductively coded to identify perceptions and behavioral responses to wildfire smoke. Seasonal wildfire smoke is an active topic on Twitter. The term "smoke" was strongly correlated with poor air quality and "unhealthy" was moderately correlated. Deductive analyses revealed a multidimensional response to wildfire smoke. Inductive analysis identified new areas of concern, such as pet and animal health. Social media is a lens through which public health professionals can assess and respond to local community needs. Findings will be used to broaden the conceptual model, enhance ongoing surveillance of community-identified health risks, and communicate protective actions.

Abstract Currently, the particulate matter with diameter less than 2.5 micron (PM2.5) pollutant has gained more concerned as can be seen from the WHO revised the air quality guideline value. The 24-hour average concentration has been strengthened from 25 µg m-3 to 15 µg m-3. However, the continuous PM2.5 monitoring system provides data on an hourly basis, which can be averaged at a 24-hour value compare with the WHO air quality guidelines. The value given by the moving average technique can be stored at the leftmost, center or rightmost hour. Three moving average PM2.5 time series would differ from the hourly observed PM2.5 data. Similarity testing by cross-correlation and Euclidean distance was performed to present a suitable 24-hour moving average time series for hourly data. The 24-hour moving average time series recorded at center is more suitable than the leftmost and rightmost 24-hour moving average time series in terms of shape and distance. It has less time lag and distance to the hourly PM2.5 time series. Comparing the 24-hour moving average time series to the WHO interim targets and the guideline value reveals PM2.5 concentration level lower than the guideline value (15 µg m-3) about 40% during the nighttime, whereas the proportion during daytime is around 28%. Also, the NAAQS of Thailand for 24-hour PM2.5 was changed from 50 µg m-3 to 37.5 µg m-3 corresponding to the interim targets 3 and 2, respectively. From this study, concentrations higher than the NAAQs level will increase from 10 to about 22%. The increase in the number of exceedances based on the same data means the state of air quality is similar. Therefore, residents may misunderstand and know the air quality becomes more severe. The government should spend more effort to reduce emissions and ambient air concentrations than earlier endeavors.

A Portable Emissions Monitoring System (PEMS) has been used to estimate ammonia emission rates from a representative fleet of 47 in-use light-duty gasoline motor vehicles over 145 on-road Real Driving Emissions (RDE) tests. The PEMS modules were carried onboard the tested vehicles and were wired such that their ceramic exhaust emission sensors were mounted directly in the tailpipe. The on-road RDE tests were conducted over an urban testing route that included residential and highway roads, uphill and downhill road segments, stop signs, traffic lights, and a school zone with a reduced speed limit. The entire vehicle test sample had an average ammonia emission rate of 114.7 mg/mile ±135.3 (StD). This would yield an estimated 2909 metric tons per year of NH3 emissions from the Wasatch fleet on-road gasoline motor vehicles. Old vehicles with aged Three-Way Catalyst (TWC) converters had higher NH3 emissions rates than newer vehicles with newer TWC converters. For instance, Tier 0, Tier I, NLEV, Tier II and Tier III vehicles had average emission rates of 563.1, 177.8, 213.6, 94.4 and 18.9 mg/mile, respectively. Carbon monoxide, and nitrogen oxides had a strong correlation with ammonia emission rates, with r ≥ 0.70. A Moderate correlation was found with vehicles’ mileage (r = 0.6), model year (r = −0.5), engine displacement (r = 0.4), and number of cylinders (r = 0.4). The outcomes highlight vehicle contributions to the atmospheric NH3 inventory and the impact of vehicle characteristics and ammonia precursor concentrations on ammonia emission rates from gasoline vehicles.

Vehicle emissions are a major source of pollution in urban communities and idling may contribute up to 34% or more to local air pollution levels. Reduced idling has been found to be an effective policy tool for improving air quality, especially around schools, where it may also improve outcomes for asthmatic children. We studied two anti-idling campaigns in Salt Lake County, Utah to understand if reduced engine idling leads to behavioral change and subsequent reduction in traffic-related air pollution exposure of the related school. We found a 38% decrease in idling time following an anti-idling campaign and an 11% decrease in the number of vehicles idling at the school drop-off zones. The air quality measurements showed improvement in the middle of the campaign, but seasonal variability as well as atmospheric inversion events had substantial effects on overall ambient pollutant concentrations. This study provides an encouraging starting point to develop more effective anti-idling campaigns to protect the health of children, school staff, and the surrounding community.

Although there is mounting evidence that suggests that air pollution is impactful to human health and educational outcomes, this is especially problematic in schools with higher air pollution levels. To understand whether all schools in an urban area are exposed to similar outdoor air quality and whether school infrastructure protects children equally indoors, we installed research-grade sensors to observe PM2.5 concentrations in indoor and outdoor settings to understand how unequal exposure to indoor and outdoor air pollution impacts indoor air quality among high- and low-income schools in Salt Lake City, Utah. These data and resulting analysis show that poor air quality may impact school settings and the potential implications with respect to environmental inequality. Based on this approach, we found that during atmospheric inversions and dust events, there was a lag ranging between 35 and 73 min for the outdoor PM2.5 concentrations to follow a similar temporal pattern as the indoor PM2.5. This lag has policy and health implications and may help to explain rising concerns regarding reduced educational outcomes related to air pollution in urban areas.

Every day around 93% of children under the age of 15 (1.8 billion children) breathe outdoor air that is so polluted it puts their health and development at serious risk. Due to the pandemic, however, ventilation of buildings using outdoor air has become an important safety technique to prevent the spread of COVID-19. With the mounting ev-idence suggesting that air pollution is impactful to human health and educational out-comes, this contradictory guidance may be problematic in schools with higher air pol-lution levels, but keeping kids COVID-19 free and in school to receive their education is now more pressing than ever. To understand if all schools in an urban area are ex-posed to similar outdoor air quality and if school infrastructure protects children equally indoors, we installed research grade sensors to observe PM2.5 concentrations in indoor and outdoor settings to understand how unequal exposure to indoor and out-door air pollution impacts indoor air quality among high- and low-income schools in Salt Lake City, Utah. Based on this approach, we found that during atmospheric inver-sions and dust events, there was a lag ranging between 35 to 73 minutes for the out-door PM2.5 concentrations to follow a similar temporal pattern as the indoor PM2.5. This lag has policy and health implications and may help to explain the rising concerns re-garding reduced educational outcomes related to air pollution in urban areas. These data and resulting analysis show that poor air quality may impact school settings, and the potential implications with respect to environmental inequality.

Abstract Smoke plume dynamic science focuses on understanding the various smoke processes that control the movement and mixing of smoke. A current challenge facing this research is providing timely and accurate smoke information for the increasing area burned by wildfires in the western USA. This chapter synthesizes smoke plume research from the past decade to evaluate the current state of science and identify future research needs. Major advances have been achieved in measurements and modeling of smoke plume rise, dispersion, transport, and superfog; interactions with fire, atmosphere, and canopy; and applications to smoke management. The biggest remaining gaps are the lack of high-resolution coupled fire, smoke, and atmospheric modeling systems, and simultaneous measurements of these components. The science of smoke plume dynamics is likely to improve through development and implementation of: improved observational capabilities and computational power; new approaches and tools for data integration; varied levels of observations, partnerships, and projects focused on field campaigns and operational management; and new efforts to implement fire and stewardship strategies and transition research on smoke dynamics into operational tools. Recent research on a number of key smoke plume dynamics has improved our understanding of coupled smoke modeling systems, modeling tools that use field campaign data, real-time smoke modeling and prediction, and smoke from duff burning. This new research will lead to better predictions of smoke production and transport, including the influence of a warmer climate on smoke.